Load Packages

library(ggplot2)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ lubridate 1.9.2     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(meta)
## Loading 'meta' package (version 6.5-0).
## Type 'help(meta)' for a brief overview.
## Readers of 'Meta-Analysis with R (Use R!)' should install
## older version of 'meta' package: https://tinyurl.com/dt4y5drs
library(PRISMAstatement)
library(skimr)
library(MASS)
## 
## Attaching package: 'MASS'
## 
## The following object is masked from 'package:dplyr':
## 
##     select
library(ggpubr)
setwd("~/Desktop/Chapter 4")
photo <- read.csv("observations_2022.csv")
summary(photo)
##     region              site            site_code          microsite        
##  Length:58015       Length:58015       Length:58015       Length:58015      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       plot           cam_ID         month                day       
##  Min.   :1.000   Min.   :1.000   Length:58015       Min.   : 3.00  
##  1st Qu.:1.000   1st Qu.:1.000   Class :character   1st Qu.: 6.00  
##  Median :1.000   Median :2.000   Mode  :character   Median :16.00  
##  Mean   :1.775   Mean   :1.527                      Mean   :13.98  
##  3rd Qu.:2.000   3rd Qu.:2.000                      3rd Qu.:22.00  
##  Max.   :4.000   Max.   :2.000                      Max.   :29.00  
##       year      shrub_density         rep        identified_by     
##  Min.   :2022   Min.   : 0.000   Min.   :    1   Length:58015      
##  1st Qu.:2022   1st Qu.: 0.000   1st Qu.: 5386   Class :character  
##  Median :2022   Median : 0.000   Median :12638   Mode  :character  
##  Mean   :2022   Mean   : 4.681   Mean   :15170                     
##  3rd Qu.:2022   3rd Qu.:11.000   3rd Qu.:24318                     
##  Max.   :2022   Max.   :14.000   Max.   :38822                     
##    filename          timestamp           animal.hit         class          
##  Length:58015       Length:58015       Min.   :0.00000   Length:58015      
##  Class :character   Class :character   1st Qu.:0.00000   Class :character  
##  Mode  :character   Mode  :character   Median :0.00000   Mode  :character  
##                                        Mean   :0.06521                     
##                                        3rd Qu.:0.00000                     
##                                        Max.   :1.00000                     
##     order              family             genus             species         
##  Length:58015       Length:58015       Length:58015       Length:58015      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##  common_name        number_of_objects
##  Length:58015       Min.   : 1.000   
##  Class :character   1st Qu.: 1.000   
##  Mode  :character   Median : 1.000   
##                     Mean   : 1.001   
##                     3rd Qu.: 1.000   
##                     Max.   :12.000
photo <- photo %>%
  filter(common_name != "Human")
photo <- photo %>%
  filter(common_name != "Human-Camera Trapper")
photo <- photo %>%
  filter(common_name != "Domestic Dog")
photo <- photo %>%
  filter(common_name != "Vehicle")
photo <- photo %>%
  dplyr::filter(common_name != "Insect")
photo <- photo %>%
  dplyr::filter(common_name != "Animal")
photo <- photo %>%
  dplyr::filter(common_name != "Bird")
photo <- photo %>%
  filter(common_name != "No CV Result")
  
count.hit <- photo %>%
  count(animal.hit) %>%
  na.omit()
summary(count.hit)
##    animal.hit         n        
##  Min.   :0.00   Min.   : 3169  
##  1st Qu.:0.25   1st Qu.:15935  
##  Median :0.50   Median :28700  
##  Mean   :0.50   Mean   :28700  
##  3rd Qu.:0.75   3rd Qu.:41466  
##  Max.   :1.00   Max.   :54232
### 2022 Had a 5.88% catch rate
### Animal Observations by Site_Code
animals_by_sitecode <- photo%>%
  group_by(site_code, microsite, common_name) %>%
  summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode <- animals_by_sitecode %>%
  filter(common_name != "Blank") 
### Animal observations by Site
animals_by_site <- photo %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site <- animals_by_site %>% filter(common_name != "Blank")
### Animal observations by Density
animals_by_density <- photo %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density <- animals_by_density %>% filter(common_name != "Blank")  %>% filter(common_name != "No CV Result")
### Total Observations 2022
Total_Observations <- photo %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank")  %>% filter(common_name != "No CV Result")
density_obvs <- merge(animals_by_density, Total_Observations, all = TRUE)
density_obvs$percent_presence <- density_obvs$captures/density_obvs$total
### Percent proportion Figure
plot1 <- ggplot(density_obvs, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot1 + scale_fill_manual(values = c("#009900", "#0066cc"))

library(emmeans)
m1 <- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs)
anova(m1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: total
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
## NULL                                     44      22806             
## microsite              1      5.4        43      22801  0.01984 *  
## common_name           26  22800.9        17          0  < 2e-16 ***
## microsite:common_name 17      0.0         0          0  1.00000    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e1 <- emmeans(m1, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
e1
## $emmeans
##  common_name                emmean      SE  df asymp.LCL asymp.UCL
##  American Robin             nonEst      NA  NA        NA        NA
##  Black-tailed Jackrabbit     5.442 0.04652 Inf    5.3512     5.534
##  Blunt-nosed Leopard Lizard  1.099 0.40825 Inf    0.2985     1.899
##  Bobcat                      1.792 0.28868 Inf    1.2260     2.358
##  Brewer's Blackbird          2.485 0.20412 Inf    2.0848     2.885
##  California Ground Squirrel  5.442 0.04652 Inf    5.3512     5.534
##  California Pocket Mouse     1.386 0.35355 Inf    0.6933     2.079
##  California Quail            3.178 0.14434 Inf    2.8952     3.461
##  California Thrasher        nonEst      NA  NA        NA        NA
##  Common Raven                3.989 0.09623 Inf    3.8004     4.178
##  Coyote                      4.489 0.07495 Inf    4.3417     4.636
##  Desert Cottontail           4.078 0.09206 Inf    3.8971     4.258
##  Desert Iguana              nonEst      NA  NA        NA        NA
##  Giant Kangaroo Rat          4.078 0.09206 Inf    3.8971     4.258
##  Great White Egret          nonEst      NA  NA        NA        NA
##  Greater Roadrunner          1.946 0.26726 Inf    1.4221     2.470
##  Heermann's Kangaroo Rat     7.680 0.01520 Inf    7.6504     7.710
##  Killdeer                   nonEst      NA  NA        NA        NA
##  Kit Fox                     2.079 0.25000 Inf    1.5895     2.569
##  Lark Sparrow                1.946 0.26726 Inf    1.4221     2.470
##  Loggerhead Shrike           2.079 0.25000 Inf    1.5895     2.569
##  Mohave Ground Squirrel     nonEst      NA  NA        NA        NA
##  Mourning Dove               1.609 0.31623 Inf    0.9896     2.229
##  Nelson's Antelope Squirrel  4.956 0.05934 Inf    4.8395     5.072
##  Red-tailed Hawk            nonEst      NA  NA        NA        NA
##  Salinas Pocket Mouse       nonEst      NA  NA        NA        NA
##  Vesper Sparrow             nonEst      NA  NA        NA        NA
## 
## Results are averaged over the levels of: microsite 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                  estimate     SE  df
##  American Robin - (Black-tailed Jackrabbit)                  nonEst     NA  NA
##  American Robin - (Blunt-nosed Leopard Lizard)               nonEst     NA  NA
##  American Robin - Bobcat                                     nonEst     NA  NA
##  American Robin - Brewer's Blackbird                         nonEst     NA  NA
##  American Robin - California Ground Squirrel                 nonEst     NA  NA
##  American Robin - California Pocket Mouse                    nonEst     NA  NA
##  American Robin - California Quail                           nonEst     NA  NA
##  American Robin - California Thrasher                        nonEst     NA  NA
##  American Robin - Common Raven                               nonEst     NA  NA
##  American Robin - Coyote                                     nonEst     NA  NA
##  American Robin - Desert Cottontail                          nonEst     NA  NA
##  American Robin - Desert Iguana                              nonEst     NA  NA
##  American Robin - Giant Kangaroo Rat                         nonEst     NA  NA
##  American Robin - Great White Egret                          nonEst     NA  NA
##  American Robin - Greater Roadrunner                         nonEst     NA  NA
##  American Robin - Heermann's Kangaroo Rat                    nonEst     NA  NA
##  American Robin - Killdeer                                   nonEst     NA  NA
##  American Robin - Kit Fox                                    nonEst     NA  NA
##  American Robin - Lark Sparrow                               nonEst     NA  NA
##  American Robin - Loggerhead Shrike                          nonEst     NA  NA
##  American Robin - Mohave Ground Squirrel                     nonEst     NA  NA
##  American Robin - Mourning Dove                              nonEst     NA  NA
##  American Robin - Nelson's Antelope Squirrel                 nonEst     NA  NA
##  American Robin - (Red-tailed Hawk)                          nonEst     NA  NA
##  American Robin - Salinas Pocket Mouse                       nonEst     NA  NA
##  American Robin - Vesper Sparrow                             nonEst     NA  NA
##  (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard)    4.3438 0.4109 Inf
##  (Black-tailed Jackrabbit) - Bobcat                          3.6507 0.2924 Inf
##  (Black-tailed Jackrabbit) - Brewer's Blackbird              2.9575 0.2094 Inf
##  (Black-tailed Jackrabbit) - California Ground Squirrel      0.0000 0.0658 Inf
##  (Black-tailed Jackrabbit) - California Pocket Mouse         4.0561 0.3566 Inf
##  (Black-tailed Jackrabbit) - California Quail                2.2644 0.1517 Inf
##  (Black-tailed Jackrabbit) - California Thrasher             nonEst     NA  NA
##  (Black-tailed Jackrabbit) - Common Raven                    1.4534 0.1069 Inf
##  (Black-tailed Jackrabbit) - Coyote                          0.9538 0.0882 Inf
##  (Black-tailed Jackrabbit) - Desert Cottontail               1.3649 0.1031 Inf
##  (Black-tailed Jackrabbit) - Desert Iguana                   nonEst     NA  NA
##  (Black-tailed Jackrabbit) - Giant Kangaroo Rat              1.3649 0.1031 Inf
##  (Black-tailed Jackrabbit) - Great White Egret               nonEst     NA  NA
##  (Black-tailed Jackrabbit) - Greater Roadrunner              3.4965 0.2713 Inf
##  (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat        -2.2378 0.0489 Inf
##  (Black-tailed Jackrabbit) - Killdeer                        nonEst     NA  NA
##  (Black-tailed Jackrabbit) - Kit Fox                         3.3630 0.2543 Inf
##  (Black-tailed Jackrabbit) - Lark Sparrow                    3.4965 0.2713 Inf
##  (Black-tailed Jackrabbit) - Loggerhead Shrike               3.3630 0.2543 Inf
##  (Black-tailed Jackrabbit) - Mohave Ground Squirrel          nonEst     NA  NA
##  (Black-tailed Jackrabbit) - Mourning Dove                   3.8330 0.3196 Inf
##  (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel      0.4866 0.0754 Inf
##  (Black-tailed Jackrabbit) - (Red-tailed Hawk)               nonEst     NA  NA
##  (Black-tailed Jackrabbit) - Salinas Pocket Mouse            nonEst     NA  NA
##  (Black-tailed Jackrabbit) - Vesper Sparrow                  nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Bobcat                      -0.6931 0.5000 Inf
##  (Blunt-nosed Leopard Lizard) - Brewer's Blackbird          -1.3863 0.4564 Inf
##  (Blunt-nosed Leopard Lizard) - California Ground Squirrel  -4.3438 0.4109 Inf
##  (Blunt-nosed Leopard Lizard) - California Pocket Mouse     -0.2877 0.5401 Inf
##  (Blunt-nosed Leopard Lizard) - California Quail            -2.0794 0.4330 Inf
##  (Blunt-nosed Leopard Lizard) - California Thrasher          nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Common Raven                -2.8904 0.4194 Inf
##  (Blunt-nosed Leopard Lizard) - Coyote                      -3.3900 0.4151 Inf
##  (Blunt-nosed Leopard Lizard) - Desert Cottontail           -2.9789 0.4185 Inf
##  (Blunt-nosed Leopard Lizard) - Desert Iguana                nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat          -2.9789 0.4185 Inf
##  (Blunt-nosed Leopard Lizard) - Great White Egret            nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Greater Roadrunner          -0.8473 0.4879 Inf
##  (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat     -6.5816 0.4085 Inf
##  (Blunt-nosed Leopard Lizard) - Killdeer                     nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Kit Fox                     -0.9808 0.4787 Inf
##  (Blunt-nosed Leopard Lizard) - Lark Sparrow                -0.8473 0.4879 Inf
##  (Blunt-nosed Leopard Lizard) - Loggerhead Shrike           -0.9808 0.4787 Inf
##  (Blunt-nosed Leopard Lizard) - Mohave Ground Squirrel       nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Mourning Dove               -0.5108 0.5164 Inf
##  (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel  -3.8572 0.4125 Inf
##  (Blunt-nosed Leopard Lizard) - (Red-tailed Hawk)            nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse         nonEst     NA  NA
##  (Blunt-nosed Leopard Lizard) - Vesper Sparrow               nonEst     NA  NA
##  Bobcat - Brewer's Blackbird                                -0.6931 0.3536 Inf
##  Bobcat - California Ground Squirrel                        -3.6507 0.2924 Inf
##  Bobcat - California Pocket Mouse                            0.4055 0.4564 Inf
##  Bobcat - California Quail                                  -1.3863 0.3227 Inf
##  Bobcat - California Thrasher                                nonEst     NA  NA
##  Bobcat - Common Raven                                      -2.1972 0.3043 Inf
##  Bobcat - Coyote                                            -2.6969 0.2982 Inf
##  Bobcat - Desert Cottontail                                 -2.2858 0.3030 Inf
##  Bobcat - Desert Iguana                                      nonEst     NA  NA
##  Bobcat - Giant Kangaroo Rat                                -2.2858 0.3030 Inf
##  Bobcat - Great White Egret                                  nonEst     NA  NA
##  Bobcat - Greater Roadrunner                                -0.1542 0.3934 Inf
##  Bobcat - Heermann's Kangaroo Rat                           -5.8884 0.2891 Inf
##  Bobcat - Killdeer                                           nonEst     NA  NA
##  Bobcat - Kit Fox                                           -0.2877 0.3819 Inf
##  Bobcat - Lark Sparrow                                      -0.1542 0.3934 Inf
##  Bobcat - Loggerhead Shrike                                 -0.2877 0.3819 Inf
##  Bobcat - Mohave Ground Squirrel                             nonEst     NA  NA
##  Bobcat - Mourning Dove                                      0.1823 0.4282 Inf
##  Bobcat - Nelson's Antelope Squirrel                        -3.1641 0.2947 Inf
##  Bobcat - (Red-tailed Hawk)                                  nonEst     NA  NA
##  Bobcat - Salinas Pocket Mouse                               nonEst     NA  NA
##  Bobcat - Vesper Sparrow                                     nonEst     NA  NA
##  Brewer's Blackbird - California Ground Squirrel            -2.9575 0.2094 Inf
##  Brewer's Blackbird - California Pocket Mouse                1.0986 0.4082 Inf
##  Brewer's Blackbird - California Quail                      -0.6931 0.2500 Inf
##  Brewer's Blackbird - California Thrasher                    nonEst     NA  NA
##  Brewer's Blackbird - Common Raven                          -1.5041 0.2257 Inf
##  Brewer's Blackbird - Coyote                                -2.0037 0.2175 Inf
##  Brewer's Blackbird - Desert Cottontail                     -1.5926 0.2239 Inf
##  Brewer's Blackbird - Desert Iguana                          nonEst     NA  NA
##  Brewer's Blackbird - Giant Kangaroo Rat                    -1.5926 0.2239 Inf
##  Brewer's Blackbird - Great White Egret                      nonEst     NA  NA
##  Brewer's Blackbird - Greater Roadrunner                     0.5390 0.3363 Inf
##  Brewer's Blackbird - Heermann's Kangaroo Rat               -5.1953 0.2047 Inf
##  Brewer's Blackbird - Killdeer                               nonEst     NA  NA
##  Brewer's Blackbird - Kit Fox                                0.4055 0.3227 Inf
##  Brewer's Blackbird - Lark Sparrow                           0.5390 0.3363 Inf
##  Brewer's Blackbird - Loggerhead Shrike                      0.4055 0.3227 Inf
##  Brewer's Blackbird - Mohave Ground Squirrel                 nonEst     NA  NA
##  Brewer's Blackbird - Mourning Dove                          0.8755 0.3764 Inf
##  Brewer's Blackbird - Nelson's Antelope Squirrel            -2.4709 0.2126 Inf
##  Brewer's Blackbird - (Red-tailed Hawk)                      nonEst     NA  NA
##  Brewer's Blackbird - Salinas Pocket Mouse                   nonEst     NA  NA
##  Brewer's Blackbird - Vesper Sparrow                         nonEst     NA  NA
##  California Ground Squirrel - California Pocket Mouse        4.0561 0.3566 Inf
##  California Ground Squirrel - California Quail               2.2644 0.1517 Inf
##  California Ground Squirrel - California Thrasher            nonEst     NA  NA
##  California Ground Squirrel - Common Raven                   1.4534 0.1069 Inf
##  California Ground Squirrel - Coyote                         0.9538 0.0882 Inf
##  California Ground Squirrel - Desert Cottontail              1.3649 0.1031 Inf
##  California Ground Squirrel - Desert Iguana                  nonEst     NA  NA
##  California Ground Squirrel - Giant Kangaroo Rat             1.3649 0.1031 Inf
##  California Ground Squirrel - Great White Egret              nonEst     NA  NA
##  California Ground Squirrel - Greater Roadrunner             3.4965 0.2713 Inf
##  California Ground Squirrel - Heermann's Kangaroo Rat       -2.2378 0.0489 Inf
##  California Ground Squirrel - Killdeer                       nonEst     NA  NA
##  California Ground Squirrel - Kit Fox                        3.3630 0.2543 Inf
##  California Ground Squirrel - Lark Sparrow                   3.4965 0.2713 Inf
##  California Ground Squirrel - Loggerhead Shrike              3.3630 0.2543 Inf
##  California Ground Squirrel - Mohave Ground Squirrel         nonEst     NA  NA
##  California Ground Squirrel - Mourning Dove                  3.8330 0.3196 Inf
##  California Ground Squirrel - Nelson's Antelope Squirrel     0.4866 0.0754 Inf
##  California Ground Squirrel - (Red-tailed Hawk)              nonEst     NA  NA
##  California Ground Squirrel - Salinas Pocket Mouse           nonEst     NA  NA
##  California Ground Squirrel - Vesper Sparrow                 nonEst     NA  NA
##  California Pocket Mouse - California Quail                 -1.7918 0.3819 Inf
##  California Pocket Mouse - California Thrasher               nonEst     NA  NA
##  California Pocket Mouse - Common Raven                     -2.6027 0.3664 Inf
##  California Pocket Mouse - Coyote                           -3.1023 0.3614 Inf
##  California Pocket Mouse - Desert Cottontail                -2.6912 0.3653 Inf
##  California Pocket Mouse - Desert Iguana                     nonEst     NA  NA
##  California Pocket Mouse - Giant Kangaroo Rat               -2.6912 0.3653 Inf
##  California Pocket Mouse - Great White Egret                 nonEst     NA  NA
##  California Pocket Mouse - Greater Roadrunner               -0.5596 0.4432 Inf
##  California Pocket Mouse - Heermann's Kangaroo Rat          -6.2939 0.3539 Inf
##  California Pocket Mouse - Killdeer                          nonEst     NA  NA
##  California Pocket Mouse - Kit Fox                          -0.6931 0.4330 Inf
##  California Pocket Mouse - Lark Sparrow                     -0.5596 0.4432 Inf
##  California Pocket Mouse - Loggerhead Shrike                -0.6931 0.4330 Inf
##  California Pocket Mouse - Mohave Ground Squirrel            nonEst     NA  NA
##  California Pocket Mouse - Mourning Dove                    -0.2231 0.4743 Inf
##  California Pocket Mouse - Nelson's Antelope Squirrel       -3.5695 0.3585 Inf
##  California Pocket Mouse - (Red-tailed Hawk)                 nonEst     NA  NA
##  California Pocket Mouse - Salinas Pocket Mouse              nonEst     NA  NA
##  California Pocket Mouse - Vesper Sparrow                    nonEst     NA  NA
##  California Quail - California Thrasher                      nonEst     NA  NA
##  California Quail - Common Raven                            -0.8109 0.1735 Inf
##  California Quail - Coyote                                  -1.3106 0.1626 Inf
##  California Quail - Desert Cottontail                       -0.8995 0.1712 Inf
##  California Quail - Desert Iguana                            nonEst     NA  NA
##  California Quail - Giant Kangaroo Rat                      -0.8995 0.1712 Inf
##  California Quail - Great White Egret                        nonEst     NA  NA
##  California Quail - Greater Roadrunner                       1.2321 0.3037 Inf
##  California Quail - Heermann's Kangaroo Rat                 -4.5021 0.1451 Inf
##  California Quail - Killdeer                                 nonEst     NA  NA
##  California Quail - Kit Fox                                  1.0986 0.2887 Inf
##  California Quail - Lark Sparrow                             1.2321 0.3037 Inf
##  California Quail - Loggerhead Shrike                        1.0986 0.2887 Inf
##  California Quail - Mohave Ground Squirrel                   nonEst     NA  NA
##  California Quail - Mourning Dove                            1.5686 0.3476 Inf
##  California Quail - Nelson's Antelope Squirrel              -1.7778 0.1561 Inf
##  California Quail - (Red-tailed Hawk)                        nonEst     NA  NA
##  California Quail - Salinas Pocket Mouse                     nonEst     NA  NA
##  California Quail - Vesper Sparrow                           nonEst     NA  NA
##  California Thrasher - Common Raven                          nonEst     NA  NA
##  California Thrasher - Coyote                                nonEst     NA  NA
##  California Thrasher - Desert Cottontail                     nonEst     NA  NA
##  California Thrasher - Desert Iguana                         nonEst     NA  NA
##  California Thrasher - Giant Kangaroo Rat                    nonEst     NA  NA
##  California Thrasher - Great White Egret                     nonEst     NA  NA
##  California Thrasher - Greater Roadrunner                    nonEst     NA  NA
##  California Thrasher - Heermann's Kangaroo Rat               nonEst     NA  NA
##  California Thrasher - Killdeer                              nonEst     NA  NA
##  California Thrasher - Kit Fox                               nonEst     NA  NA
##  California Thrasher - Lark Sparrow                          nonEst     NA  NA
##  California Thrasher - Loggerhead Shrike                     nonEst     NA  NA
##  California Thrasher - Mohave Ground Squirrel                nonEst     NA  NA
##  California Thrasher - Mourning Dove                         nonEst     NA  NA
##  California Thrasher - Nelson's Antelope Squirrel            nonEst     NA  NA
##  California Thrasher - (Red-tailed Hawk)                     nonEst     NA  NA
##  California Thrasher - Salinas Pocket Mouse                  nonEst     NA  NA
##  California Thrasher - Vesper Sparrow                        nonEst     NA  NA
##  Common Raven - Coyote                                      -0.4997 0.1220 Inf
##  Common Raven - Desert Cottontail                           -0.0886 0.1332 Inf
##  Common Raven - Desert Iguana                                nonEst     NA  NA
##  Common Raven - Giant Kangaroo Rat                          -0.0886 0.1332 Inf
##  Common Raven - Great White Egret                            nonEst     NA  NA
##  Common Raven - Greater Roadrunner                           2.0431 0.2841 Inf
##  Common Raven - Heermann's Kangaroo Rat                     -3.6912 0.0974 Inf
##  Common Raven - Killdeer                                     nonEst     NA  NA
##  Common Raven - Kit Fox                                      1.9095 0.2679 Inf
##  Common Raven - Lark Sparrow                                 2.0431 0.2841 Inf
##  Common Raven - Loggerhead Shrike                            1.9095 0.2679 Inf
##  Common Raven - Mohave Ground Squirrel                       nonEst     NA  NA
##  Common Raven - Mourning Dove                                2.3795 0.3305 Inf
##  Common Raven - Nelson's Antelope Squirrel                  -0.9668 0.1131 Inf
##  Common Raven - (Red-tailed Hawk)                            nonEst     NA  NA
##  Common Raven - Salinas Pocket Mouse                         nonEst     NA  NA
##  Common Raven - Vesper Sparrow                               nonEst     NA  NA
##  Coyote - Desert Cottontail                                  0.4111 0.1187 Inf
##  Coyote - Desert Iguana                                      nonEst     NA  NA
##  Coyote - Giant Kangaroo Rat                                 0.4111 0.1187 Inf
##  Coyote - Great White Egret                                  nonEst     NA  NA
##  Coyote - Greater Roadrunner                                 2.5427 0.2776 Inf
##  Coyote - Heermann's Kangaroo Rat                           -3.1915 0.0765 Inf
##  Coyote - Killdeer                                           nonEst     NA  NA
##  Coyote - Kit Fox                                            2.4092 0.2610 Inf
##  Coyote - Lark Sparrow                                       2.5427 0.2776 Inf
##  Coyote - Loggerhead Shrike                                  2.4092 0.2610 Inf
##  Coyote - Mohave Ground Squirrel                             nonEst     NA  NA
##  Coyote - Mourning Dove                                      2.8792 0.3250 Inf
##  Coyote - Nelson's Antelope Squirrel                        -0.4672 0.0956 Inf
##  Coyote - (Red-tailed Hawk)                                  nonEst     NA  NA
##  Coyote - Salinas Pocket Mouse                               nonEst     NA  NA
##  Coyote - Vesper Sparrow                                     nonEst     NA  NA
##  Desert Cottontail - Desert Iguana                           nonEst     NA  NA
##  Desert Cottontail - Giant Kangaroo Rat                      0.0000 0.1302 Inf
##  Desert Cottontail - Great White Egret                       nonEst     NA  NA
##  Desert Cottontail - Greater Roadrunner                      2.1316 0.2827 Inf
##  Desert Cottontail - Heermann's Kangaroo Rat                -3.6026 0.0933 Inf
##  Desert Cottontail - Killdeer                                nonEst     NA  NA
##  Desert Cottontail - Kit Fox                                 1.9981 0.2664 Inf
##  Desert Cottontail - Lark Sparrow                            2.1316 0.2827 Inf
##  Desert Cottontail - Loggerhead Shrike                       1.9981 0.2664 Inf
##  Desert Cottontail - Mohave Ground Squirrel                  nonEst     NA  NA
##  Desert Cottontail - Mourning Dove                           2.4681 0.3294 Inf
##  Desert Cottontail - Nelson's Antelope Squirrel             -0.8783 0.1095 Inf
##  Desert Cottontail - (Red-tailed Hawk)                       nonEst     NA  NA
##  Desert Cottontail - Salinas Pocket Mouse                    nonEst     NA  NA
##  Desert Cottontail - Vesper Sparrow                          nonEst     NA  NA
##  Desert Iguana - Giant Kangaroo Rat                          nonEst     NA  NA
##  Desert Iguana - Great White Egret                           nonEst     NA  NA
##  Desert Iguana - Greater Roadrunner                          nonEst     NA  NA
##  Desert Iguana - Heermann's Kangaroo Rat                     nonEst     NA  NA
##  Desert Iguana - Killdeer                                    nonEst     NA  NA
##  Desert Iguana - Kit Fox                                     nonEst     NA  NA
##  Desert Iguana - Lark Sparrow                                nonEst     NA  NA
##  Desert Iguana - Loggerhead Shrike                           nonEst     NA  NA
##  Desert Iguana - Mohave Ground Squirrel                      nonEst     NA  NA
##  Desert Iguana - Mourning Dove                               nonEst     NA  NA
##  Desert Iguana - Nelson's Antelope Squirrel                  nonEst     NA  NA
##  Desert Iguana - (Red-tailed Hawk)                           nonEst     NA  NA
##  Desert Iguana - Salinas Pocket Mouse                        nonEst     NA  NA
##  Desert Iguana - Vesper Sparrow                              nonEst     NA  NA
##  Giant Kangaroo Rat - Great White Egret                      nonEst     NA  NA
##  Giant Kangaroo Rat - Greater Roadrunner                     2.1316 0.2827 Inf
##  Giant Kangaroo Rat - Heermann's Kangaroo Rat               -3.6026 0.0933 Inf
##  Giant Kangaroo Rat - Killdeer                               nonEst     NA  NA
##  Giant Kangaroo Rat - Kit Fox                                1.9981 0.2664 Inf
##  Giant Kangaroo Rat - Lark Sparrow                           2.1316 0.2827 Inf
##  Giant Kangaroo Rat - Loggerhead Shrike                      1.9981 0.2664 Inf
##  Giant Kangaroo Rat - Mohave Ground Squirrel                 nonEst     NA  NA
##  Giant Kangaroo Rat - Mourning Dove                          2.4681 0.3294 Inf
##  Giant Kangaroo Rat - Nelson's Antelope Squirrel            -0.8783 0.1095 Inf
##  Giant Kangaroo Rat - (Red-tailed Hawk)                      nonEst     NA  NA
##  Giant Kangaroo Rat - Salinas Pocket Mouse                   nonEst     NA  NA
##  Giant Kangaroo Rat - Vesper Sparrow                         nonEst     NA  NA
##  Great White Egret - Greater Roadrunner                      nonEst     NA  NA
##  Great White Egret - Heermann's Kangaroo Rat                 nonEst     NA  NA
##  Great White Egret - Killdeer                                nonEst     NA  NA
##  Great White Egret - Kit Fox                                 nonEst     NA  NA
##  Great White Egret - Lark Sparrow                            nonEst     NA  NA
##  Great White Egret - Loggerhead Shrike                       nonEst     NA  NA
##  Great White Egret - Mohave Ground Squirrel                  nonEst     NA  NA
##  Great White Egret - Mourning Dove                           nonEst     NA  NA
##  Great White Egret - Nelson's Antelope Squirrel              nonEst     NA  NA
##  Great White Egret - (Red-tailed Hawk)                       nonEst     NA  NA
##  Great White Egret - Salinas Pocket Mouse                    nonEst     NA  NA
##  Great White Egret - Vesper Sparrow                          nonEst     NA  NA
##  Greater Roadrunner - Heermann's Kangaroo Rat               -5.7343 0.2677 Inf
##  Greater Roadrunner - Killdeer                               nonEst     NA  NA
##  Greater Roadrunner - Kit Fox                               -0.1335 0.3660 Inf
##  Greater Roadrunner - Lark Sparrow                           0.0000 0.3780 Inf
##  Greater Roadrunner - Loggerhead Shrike                     -0.1335 0.3660 Inf
##  Greater Roadrunner - Mohave Ground Squirrel                 nonEst     NA  NA
##  Greater Roadrunner - Mourning Dove                          0.3365 0.4140 Inf
##  Greater Roadrunner - Nelson's Antelope Squirrel            -3.0099 0.2738 Inf
##  Greater Roadrunner - (Red-tailed Hawk)                      nonEst     NA  NA
##  Greater Roadrunner - Salinas Pocket Mouse                   nonEst     NA  NA
##  Greater Roadrunner - Vesper Sparrow                         nonEst     NA  NA
##  Heermann's Kangaroo Rat - Killdeer                          nonEst     NA  NA
##  Heermann's Kangaroo Rat - Kit Fox                           5.6007 0.2505 Inf
##  Heermann's Kangaroo Rat - Lark Sparrow                      5.7343 0.2677 Inf
##  Heermann's Kangaroo Rat - Loggerhead Shrike                 5.6007 0.2505 Inf
##  Heermann's Kangaroo Rat - Mohave Ground Squirrel            nonEst     NA  NA
##  Heermann's Kangaroo Rat - Mourning Dove                     6.0707 0.3166 Inf
##  Heermann's Kangaroo Rat - Nelson's Antelope Squirrel        2.7243 0.0613 Inf
##  Heermann's Kangaroo Rat - (Red-tailed Hawk)                 nonEst     NA  NA
##  Heermann's Kangaroo Rat - Salinas Pocket Mouse              nonEst     NA  NA
##  Heermann's Kangaroo Rat - Vesper Sparrow                    nonEst     NA  NA
##  Killdeer - Kit Fox                                          nonEst     NA  NA
##  Killdeer - Lark Sparrow                                     nonEst     NA  NA
##  Killdeer - Loggerhead Shrike                                nonEst     NA  NA
##  Killdeer - Mohave Ground Squirrel                           nonEst     NA  NA
##  Killdeer - Mourning Dove                                    nonEst     NA  NA
##  Killdeer - Nelson's Antelope Squirrel                       nonEst     NA  NA
##  Killdeer - (Red-tailed Hawk)                                nonEst     NA  NA
##  Killdeer - Salinas Pocket Mouse                             nonEst     NA  NA
##  Killdeer - Vesper Sparrow                                   nonEst     NA  NA
##  Kit Fox - Lark Sparrow                                      0.1335 0.3660 Inf
##  Kit Fox - Loggerhead Shrike                                 0.0000 0.3536 Inf
##  Kit Fox - Mohave Ground Squirrel                            nonEst     NA  NA
##  Kit Fox - Mourning Dove                                     0.4700 0.4031 Inf
##  Kit Fox - Nelson's Antelope Squirrel                       -2.8764 0.2569 Inf
##  Kit Fox - (Red-tailed Hawk)                                 nonEst     NA  NA
##  Kit Fox - Salinas Pocket Mouse                              nonEst     NA  NA
##  Kit Fox - Vesper Sparrow                                    nonEst     NA  NA
##  Lark Sparrow - Loggerhead Shrike                           -0.1335 0.3660 Inf
##  Lark Sparrow - Mohave Ground Squirrel                       nonEst     NA  NA
##  Lark Sparrow - Mourning Dove                                0.3365 0.4140 Inf
##  Lark Sparrow - Nelson's Antelope Squirrel                  -3.0099 0.2738 Inf
##  Lark Sparrow - (Red-tailed Hawk)                            nonEst     NA  NA
##  Lark Sparrow - Salinas Pocket Mouse                         nonEst     NA  NA
##  Lark Sparrow - Vesper Sparrow                               nonEst     NA  NA
##  Loggerhead Shrike - Mohave Ground Squirrel                  nonEst     NA  NA
##  Loggerhead Shrike - Mourning Dove                           0.4700 0.4031 Inf
##  Loggerhead Shrike - Nelson's Antelope Squirrel             -2.8764 0.2569 Inf
##  Loggerhead Shrike - (Red-tailed Hawk)                       nonEst     NA  NA
##  Loggerhead Shrike - Salinas Pocket Mouse                    nonEst     NA  NA
##  Loggerhead Shrike - Vesper Sparrow                          nonEst     NA  NA
##  Mohave Ground Squirrel - Mourning Dove                      nonEst     NA  NA
##  Mohave Ground Squirrel - Nelson's Antelope Squirrel         nonEst     NA  NA
##  Mohave Ground Squirrel - (Red-tailed Hawk)                  nonEst     NA  NA
##  Mohave Ground Squirrel - Salinas Pocket Mouse               nonEst     NA  NA
##  Mohave Ground Squirrel - Vesper Sparrow                     nonEst     NA  NA
##  Mourning Dove - Nelson's Antelope Squirrel                 -3.3464 0.3217 Inf
##  Mourning Dove - (Red-tailed Hawk)                           nonEst     NA  NA
##  Mourning Dove - Salinas Pocket Mouse                        nonEst     NA  NA
##  Mourning Dove - Vesper Sparrow                              nonEst     NA  NA
##  Nelson's Antelope Squirrel - (Red-tailed Hawk)              nonEst     NA  NA
##  Nelson's Antelope Squirrel - Salinas Pocket Mouse           nonEst     NA  NA
##  Nelson's Antelope Squirrel - Vesper Sparrow                 nonEst     NA  NA
##  (Red-tailed Hawk) - Salinas Pocket Mouse                    nonEst     NA  NA
##  (Red-tailed Hawk) - Vesper Sparrow                          nonEst     NA  NA
##  Salinas Pocket Mouse - Vesper Sparrow                       nonEst     NA  NA
##  z.ratio p.value
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##   10.572  <.0001
##   12.485  <.0001
##   14.127  <.0001
##    0.000  1.0000
##   11.374  <.0001
##   14.931  <.0001
##       NA      NA
##   13.598  <.0001
##   10.812  <.0001
##   13.233  <.0001
##       NA      NA
##   13.233  <.0001
##       NA      NA
##   12.889  <.0001
##  -45.721  <.0001
##       NA      NA
##   13.225  <.0001
##   12.889  <.0001
##   13.225  <.0001
##       NA      NA
##   11.992  <.0001
##    6.453  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##   -1.386  0.9999
##   -3.037  0.3141
##  -10.572  <.0001
##   -0.533  1.0000
##   -4.802  0.0005
##       NA      NA
##   -6.891  <.0001
##   -8.167  <.0001
##   -7.118  <.0001
##       NA      NA
##   -7.118  <.0001
##       NA      NA
##   -1.736  0.9948
##  -16.110  <.0001
##       NA      NA
##   -2.049  0.9559
##   -1.736  0.9948
##   -2.049  0.9559
##       NA      NA
##   -0.989  1.0000
##   -9.350  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##   -1.961  0.9737
##  -12.485  <.0001
##    0.888  1.0000
##   -4.295  0.0051
##       NA      NA
##   -7.221  <.0001
##   -9.042  <.0001
##   -7.544  <.0001
##       NA      NA
##   -7.544  <.0001
##       NA      NA
##   -0.392  1.0000
##  -20.370  <.0001
##       NA      NA
##   -0.753  1.0000
##   -0.392  1.0000
##   -0.753  1.0000
##       NA      NA
##    0.426  1.0000
##  -10.736  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##  -14.127  <.0001
##    2.691  0.5827
##   -2.773  0.5157
##       NA      NA
##   -6.665  <.0001
##   -9.215  <.0001
##   -7.112  <.0001
##       NA      NA
##   -7.112  <.0001
##       NA      NA
##    1.603  0.9985
##  -25.381  <.0001
##       NA      NA
##    1.256  1.0000
##    1.603  0.9985
##    1.256  1.0000
##       NA      NA
##    2.326  0.8464
##  -11.624  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##   11.374  <.0001
##   14.931  <.0001
##       NA      NA
##   13.598  <.0001
##   10.812  <.0001
##   13.233  <.0001
##       NA      NA
##   13.233  <.0001
##       NA      NA
##   12.889  <.0001
##  -45.721  <.0001
##       NA      NA
##   13.225  <.0001
##   12.889  <.0001
##   13.225  <.0001
##       NA      NA
##   11.992  <.0001
##    6.453  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##   -4.692  0.0009
##       NA      NA
##   -7.103  <.0001
##   -8.584  <.0001
##   -7.366  <.0001
##       NA      NA
##   -7.366  <.0001
##       NA      NA
##   -1.263  1.0000
##  -17.785  <.0001
##       NA      NA
##   -1.601  0.9985
##   -1.263  1.0000
##   -1.601  0.9985
##       NA      NA
##   -0.470  1.0000
##   -9.957  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##   -4.675  0.0009
##   -8.058  <.0001
##   -5.254  0.0001
##       NA      NA
##   -5.254  0.0001
##       NA      NA
##    4.056  0.0135
##  -31.020  <.0001
##       NA      NA
##    3.806  0.0343
##    4.056  0.0135
##    3.806  0.0343
##       NA      NA
##    4.513  0.0020
##  -11.392  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##   -4.096  0.0115
##   -0.665  1.0000
##       NA      NA
##   -0.665  1.0000
##       NA      NA
##    7.192  <.0001
##  -37.890  <.0001
##       NA      NA
##    7.128  <.0001
##    7.192  <.0001
##    7.128  <.0001
##       NA      NA
##    7.199  <.0001
##   -8.552  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##    3.463  0.1049
##       NA      NA
##    3.463  0.1049
##       NA      NA
##    9.161  <.0001
##  -41.731  <.0001
##       NA      NA
##    9.231  <.0001
##    9.161  <.0001
##    9.231  <.0001
##       NA      NA
##    8.859  <.0001
##   -4.887  0.0003
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##    0.000  1.0000
##       NA      NA
##    7.541  <.0001
##  -38.612  <.0001
##       NA      NA
##    7.500  <.0001
##    7.541  <.0001
##    7.500  <.0001
##       NA      NA
##    7.494  <.0001
##   -8.019  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##    7.541  <.0001
##  -38.612  <.0001
##       NA      NA
##    7.500  <.0001
##    7.541  <.0001
##    7.500  <.0001
##       NA      NA
##    7.494  <.0001
##   -8.019  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##  -21.421  <.0001
##       NA      NA
##   -0.365  1.0000
##    0.000  1.0000
##   -0.365  1.0000
##       NA      NA
##    0.813  1.0000
##  -10.994  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##   22.362  <.0001
##   21.421  <.0001
##   22.362  <.0001
##       NA      NA
##   19.175  <.0001
##   44.476  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##    0.365  1.0000
##    0.000  1.0000
##       NA      NA
##    1.166  1.0000
##  -11.195  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##   -0.365  1.0000
##       NA      NA
##    0.813  1.0000
##  -10.994  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##    1.166  1.0000
##  -11.195  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##  -10.401  <.0001
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
##       NA      NA
## 
## Results are averaged over the levels of: microsite 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 27 estimates
animals_density <- photo %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density <- animals_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")

All current PCOA code for 2022 only

### PCA
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.6-4
library(ape) ### For PCOA
## 
## Attaching package: 'ape'
## The following object is masked from 'package:ggpubr':
## 
##     rotate
## The following object is masked from 'package:dplyr':
## 
##     where
pca_data <- animals_density ### Created new df for pca data
pca_data <- pca_data %>%
  spread(common_name, captures) %>%
  ungroup() %>%
  dplyr::select(-site_code, -microsite, -plot) %>%
  replace(is.na(.),0)
dim(pca_data)
## [1] 22 28
env <- read.csv("environment.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)
## [1] 22  5
m01 <- adonis(pca_data ~ microsite*shrub_density, data = env)
## 'adonis' will be deprecated: use 'adonis2' instead
m01
## $aov.tab
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##               Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)
## microsite      1    0.2242 0.22425 0.76358 0.03600  0.613
## shrub_density  1    0.4245 0.42452 1.44550 0.06815  0.211
## Residuals     19    5.5799 0.29368         0.89584       
## Total         21    6.2287                 1.00000       
## 
## $call
## adonis(formula = pca_data ~ microsite * shrub_density, data = env)
## 
## $coefficients
##                          shrub_density American Robin Black-tailed Jackrabbit
## (Intercept)                  -4.757692   5.000000e-02              -13.455128
## microsite1                   -6.857692  -5.000000e-02              -18.955128
## shrub_density                 1.876923   1.823528e-17                4.184615
## microsite1:shrub_density            NA             NA                      NA
##                          Blunt-nosed Leopard Lizard     Bobcat
## (Intercept)                               0.9987179 -0.6602564
## microsite1                                0.8987179 -1.1602564
## shrub_density                            -0.1538462  0.1692308
## microsite1:shrub_density                         NA         NA
##                          Brewer's Blackbird California Ground Squirrel
## (Intercept)                      0.18717949                  20.411538
## microsite1                      -0.21282051                  13.511538
## shrub_density                    0.06153846                  -1.815385
## microsite1:shrub_density                 NA                         NA
##                          California Pocket Mouse California Quail
## (Intercept)                           0.35641026       -4.2487179
## microsite1                            0.15641026       -5.6487179
## shrub_density                        -0.03076923        0.9538462
## microsite1:shrub_density                      NA               NA
##                          California Thrasher Common Raven     Coyote
## (Intercept)                       -1.4743590    0.7910256  1.4923077
## microsite1                        -1.4743590   -3.5089744 -4.1076923
## shrub_density                      0.2769231    0.3230769  0.4769231
## microsite1:shrub_density                  NA           NA         NA
##                          Desert Cottontail Desert Iguana Giant Kangaroo Rat
## (Intercept)                     -24.729487    2.07820513          6.9935897
## microsite1                      -26.629487   -1.82179487          4.4935897
## shrub_density                     4.861538   -0.01538462         -0.7692308
## microsite1:shrub_density                NA            NA                 NA
##                          Great White Egret Greater Roadrunner
## (Intercept)                   5.000000e-02         -4.3730769
## microsite1                   -5.000000e-02         -4.4730769
## shrub_density                 1.076807e-17          0.8307692
## microsite1:shrub_density                NA                 NA
##                          Heermann's Kangaroo Rat      Killdeer     Kit Fox
## (Intercept)                           140.330769  5.000000e-02  0.44487179
## microsite1                             59.130769 -5.000000e-02  0.14487179
## shrub_density                          -7.707692  6.937479e-18 -0.01538462
## microsite1:shrub_density                      NA            NA          NA
##                          Lark Sparrow Loggerhead Shrike Mohave Ground Squirrel
## (Intercept)                -1.8384615        -0.5935897             0.81923077
## microsite1                 -2.2384615        -0.8935897             0.71923077
## shrub_density               0.3846154         0.1692308            -0.09230769
## microsite1:shrub_density           NA                NA                     NA
##                          Mourning Dove Nelson's Antelope Squirrel
## (Intercept)                 -1.9384615                  18.984615
## microsite1                  -2.1384615                  13.784615
## shrub_density                0.3846154                  -2.246154
## microsite1:shrub_density            NA                         NA
##                          Red-tailed Hawk Salinas Pocket Mouse Vesper Sparrow
## (Intercept)                   -0.5641026         5.000000e-02   5.000000e-02
## microsite1                    -0.5641026        -5.000000e-02  -5.000000e-02
## shrub_density                  0.1076923         1.076807e-17   6.937479e-18
## microsite1:shrub_density              NA                   NA             NA
## 
## $coef.sites
##                                    1           2           3           4
## (Intercept)               0.72187241  0.84634032  1.11978420  0.78585052
## microsite1                0.19194029  0.33115230  0.42744428  0.26477687
## shrub_density            -0.03063205 -0.05640075 -0.06512504 -0.03711672
## microsite1:shrub_density          NA          NA          NA          NA
##                                   5           6           7           8
## (Intercept)               0.7805666  0.70692759  0.95262164  0.80543797
## microsite1                0.2293742  0.15739580  0.22071669  0.25186788
## shrub_density            -0.0393115 -0.02414532 -0.02857079 -0.03727318
## microsite1:shrub_density         NA          NA          NA          NA
##                                   9         10          11          12
## (Intercept)               1.7428122  1.4970441  0.99852391  1.06366871
## microsite1                1.0220992  0.8596173  0.44864549  0.53735308
## shrub_density            -0.1847971 -0.1548735 -0.07052619 -0.08656954
## microsite1:shrub_density         NA         NA          NA          NA
##                                   13           14          15          16
## (Intercept)               0.82385406  0.795551518  0.79238103  0.84065136
## microsite1                0.12690322  0.021850057  0.21124835  0.30308675
## shrub_density            -0.02568071 -0.008853955 -0.02915258 -0.04325977
## microsite1:shrub_density          NA           NA          NA          NA
##                                   17          18          19          20
## (Intercept)               0.38555800  0.50980072  0.68786923  0.55515661
## microsite1               -0.59216512 -0.43255379 -0.20343288 -0.30788751
## shrub_density             0.09323238  0.06736799  0.02712249  0.04455634
## microsite1:shrub_density          NA          NA          NA          NA
##                                    21          22
## (Intercept)               0.881696341  0.64736295
## microsite1                0.013954406 -0.20302121
## shrub_density            -0.001421805  0.02954886
## microsite1:shrub_density           NA          NA
## 
## $f.perms
##               [,1]       [,2]
##    [1,] 1.17523680 1.35296523
##    [2,] 0.98166642 0.78004529
##    [3,] 0.40043152 1.55001892
##    [4,] 0.31585544 0.82015476
##    [5,] 1.77201134 3.11595392
##    [6,] 1.23814875 2.15375009
##    [7,] 0.37267057 0.81109910
##    [8,] 1.04940680 0.75474965
##    [9,] 1.50863219 1.93679319
##   [10,] 0.65908961 1.64324425
##   [11,] 0.62490843 0.61053335
##   [12,] 1.72810053 2.81913133
##   [13,] 0.56427845 1.07807206
##   [14,] 0.32257652 0.42267195
##   [15,] 0.42170264 0.72860630
##   [16,] 1.08603625 1.13489663
##   [17,] 1.75691225 0.63314896
##   [18,] 0.37031606 1.31843237
##   [19,] 0.81095298 2.16315222
##   [20,] 2.53012166 1.45869479
##   [21,] 1.16418008 0.88624093
##   [22,] 0.44793398 0.58256970
##   [23,] 1.55247004 0.19280301
##   [24,] 0.82778860 1.93641046
##   [25,] 1.48826244 3.68241342
##   [26,] 0.56990525 1.72230221
##   [27,] 0.22126505 1.22268318
##   [28,] 0.34594658 1.03154611
##   [29,] 2.63897564 1.96627396
##   [30,] 0.76933751 0.39674199
##   [31,] 0.57609944 1.73548537
##   [32,] 1.32117359 1.44365393
##   [33,] 0.94641346 0.95836573
##   [34,] 0.92627584 3.33801241
##   [35,] 1.40526510 1.36674159
##   [36,] 2.17450145 0.86843106
##   [37,] 0.50154215 1.08691669
##   [38,] 1.11395102 0.71755676
##   [39,] 0.64133810 0.87063706
##   [40,] 0.43348240 0.36842641
##   [41,] 1.43259538 1.14958849
##   [42,] 1.25117092 0.74648519
##   [43,] 0.77958352 0.36754982
##   [44,] 0.92504011 1.60079349
##   [45,] 0.55793059 0.84906242
##   [46,] 1.12795379 1.40404071
##   [47,] 0.75102598 0.23198349
##   [48,] 0.24981999 1.41797157
##   [49,] 1.03430620 1.57316694
##   [50,] 1.31815263 0.96738647
##   [51,] 0.98454396 0.26428966
##   [52,] 0.32770954 0.75141108
##   [53,] 0.88856952 1.51162614
##   [54,] 0.98978911 0.32633876
##   [55,] 1.72449280 1.03682873
##   [56,] 0.78350859 0.96128812
##   [57,] 0.99896637 0.79036131
##   [58,] 1.95992400 1.49567258
##   [59,] 1.17256443 0.56493397
##   [60,] 1.73408435 1.38771636
##   [61,] 2.40040427 0.94766646
##   [62,] 0.65001742 1.82871817
##   [63,] 0.88626891 1.63947519
##   [64,] 1.85930496 1.19317141
##   [65,] 0.74873396 1.12989295
##   [66,] 0.86150850 1.24418373
##   [67,] 0.54840433 2.54703291
##   [68,] 0.40323732 0.92932382
##   [69,] 1.32357596 1.67204490
##   [70,] 0.55850815 0.41373430
##   [71,] 0.67077213 1.49705641
##   [72,] 0.71988173 0.96679118
##   [73,] 1.69939153 1.23519182
##   [74,] 0.59986991 0.70684895
##   [75,] 0.74801044 0.87525509
##   [76,] 0.54157497 0.55964961
##   [77,] 1.10588050 1.01637393
##   [78,] 0.36061275 0.75608988
##   [79,] 0.88592193 1.01024122
##   [80,] 0.35030525 0.82622292
##   [81,] 0.46448632 1.26086788
##   [82,] 0.76056464 0.48575366
##   [83,] 1.21124575 0.85114903
##   [84,] 2.21803356 1.50961213
##   [85,] 0.39878234 0.72572007
##   [86,] 1.31642125 0.86826125
##   [87,] 1.83692043 0.33226833
##   [88,] 0.83305641 0.72073502
##   [89,] 1.97551244 2.78762546
##   [90,] 0.29906903 0.52789401
##   [91,] 0.52455266 0.18369849
##   [92,] 0.52828746 0.29119005
##   [93,] 0.73460754 0.16090542
##   [94,] 0.79779516 0.60638179
##   [95,] 1.49942315 0.93383091
##   [96,] 0.98669740 1.07460793
##   [97,] 1.78769602 1.83056729
##   [98,] 0.94901241 2.59566234
##   [99,] 1.10579718 1.50049581
##  [100,] 0.34039449 1.55964177
##  [101,] 0.39380959 1.70449358
##  [102,] 0.85243527 0.58430886
##  [103,] 2.27098535 1.26212251
##  [104,] 1.11178706 0.83923447
##  [105,] 1.58612262 1.68470248
##  [106,] 0.53677423 0.68768146
##  [107,] 0.73353177 1.67882140
##  [108,] 2.22572882 0.60095118
##  [109,] 0.99709257 0.94447619
##  [110,] 0.53319492 0.83939966
##  [111,] 2.41463602 0.40208802
##  [112,] 0.58081286 0.81602722
##  [113,] 0.63422009 0.60187715
##  [114,] 1.03709826 0.62547736
##  [115,] 1.17566804 0.88287741
##  [116,] 0.78230946 0.62917487
##  [117,] 1.31134918 1.18608502
##  [118,] 0.86420583 1.07268352
##  [119,] 1.63913957 1.26762894
##  [120,] 0.76676592 0.34208201
##  [121,] 0.37769859 0.48225204
##  [122,] 1.73149546 0.45368139
##  [123,] 0.65904111 1.12316717
##  [124,] 0.76141451 0.91132467
##  [125,] 0.36690933 0.86673144
##  [126,] 2.21094103 0.98405092
##  [127,] 0.54713052 1.70549943
##  [128,] 1.17991664 1.30055439
##  [129,] 0.84915362 1.87307529
##  [130,] 6.15329061 0.30255202
##  [131,] 0.81973085 0.19647510
##  [132,] 0.44148353 0.57954235
##  [133,] 0.55221651 1.66479135
##  [134,] 0.35168690 0.59607093
##  [135,] 0.45864978 0.99585775
##  [136,] 0.43473756 0.61437803
##  [137,] 0.27427950 0.89825134
##  [138,] 1.00771041 1.21801826
##  [139,] 0.51640442 1.73028957
##  [140,] 1.44850041 0.97569989
##  [141,] 1.28025758 0.90976117
##  [142,] 1.51208076 1.12374210
##  [143,] 1.38468973 1.14830694
##  [144,] 0.16580304 0.68701881
##  [145,] 1.48523123 0.63146145
##  [146,] 0.85952686 0.67717482
##  [147,] 0.90346360 1.40588456
##  [148,] 0.40960926 0.86961755
##  [149,] 0.53942301 0.42404972
##  [150,] 2.81302459 0.20193402
##  [151,] 1.49024352 1.06039506
##  [152,] 0.35287026 1.54308917
##  [153,] 0.90770107 1.22737632
##  [154,] 5.01693297 0.27677468
##  [155,] 2.05752276 0.74942774
##  [156,] 0.58195553 1.97370035
##  [157,] 2.01794166 0.71379195
##  [158,] 1.94994886 1.71222732
##  [159,] 0.74160667 0.24215209
##  [160,] 1.28831871 1.73011376
##  [161,] 2.28482079 1.02781860
##  [162,] 1.13813635 1.03585628
##  [163,] 2.33044019 1.93813277
##  [164,] 1.50164503 1.18109313
##  [165,] 0.53605989 1.15618162
##  [166,] 0.86263551 0.85288379
##  [167,] 0.48129947 1.40481328
##  [168,] 0.79215626 1.10017742
##  [169,] 0.21298044 0.97004066
##  [170,] 0.56225825 3.79992133
##  [171,] 0.73520565 0.60639945
##  [172,] 1.56164105 1.04826178
##  [173,] 0.89501412 0.47155934
##  [174,] 1.41717942 0.43967105
##  [175,] 0.46508197 1.87725365
##  [176,] 0.52007253 1.17855740
##  [177,] 1.87272533 1.33145313
##  [178,] 0.96115597 0.42851589
##  [179,] 0.68814453 0.49398734
##  [180,] 0.73862333 1.19968419
##  [181,] 1.42414048 0.70469990
##  [182,] 1.07609563 0.55795268
##  [183,] 1.79718775 0.16113117
##  [184,] 0.80853271 1.85703947
##  [185,] 0.53878813 0.19631408
##  [186,] 0.90432125 3.03457957
##  [187,] 1.15039442 0.87475891
##  [188,] 0.58582879 1.74347835
##  [189,] 0.65728535 3.32398877
##  [190,] 1.36358418 1.44692331
##  [191,] 1.25123602 0.60940827
##  [192,] 1.84734567 0.47638652
##  [193,] 1.56749406 0.29766262
##  [194,] 0.51697633 0.75896313
##  [195,] 0.37915331 1.02776414
##  [196,] 0.41635885 1.43760455
##  [197,] 0.93319680 0.26067501
##  [198,] 0.81190192 1.25877157
##  [199,] 1.25909006 0.80491767
##  [200,] 0.96257904 1.33618677
##  [201,] 1.12477387 1.22156557
##  [202,] 1.23028791 2.09505753
##  [203,] 0.91438743 1.41261189
##  [204,] 0.84338626 0.96498502
##  [205,] 0.81912427 1.21415743
##  [206,] 1.12777875 0.68984790
##  [207,] 1.21881671 0.96833117
##  [208,] 0.22845803 0.43374643
##  [209,] 0.90603160 0.62657523
##  [210,] 0.74676878 0.75838581
##  [211,] 1.15406677 0.63395439
##  [212,] 0.40570586 1.03063636
##  [213,] 1.28251043 1.24998656
##  [214,] 0.77914842 1.14648488
##  [215,] 0.66271326 0.88415041
##  [216,] 1.18353057 0.51893930
##  [217,] 1.32689386 0.56857641
##  [218,] 1.06828295 0.68132684
##  [219,] 0.79433840 0.85349323
##  [220,] 0.96374493 0.55768203
##  [221,] 0.83610534 0.31109352
##  [222,] 1.05040080 0.44337876
##  [223,] 0.79389144 0.68863379
##  [224,] 0.97117005 1.17646976
##  [225,] 2.73388238 0.91129503
##  [226,] 0.65423382 1.83973636
##  [227,] 0.77495138 1.43598277
##  [228,] 0.60914691 0.51248415
##  [229,] 0.59242726 0.78418435
##  [230,] 1.13975808 0.99329550
##  [231,] 0.77905525 1.13669450
##  [232,] 0.57237902 0.72361181
##  [233,] 0.27856643 1.30751517
##  [234,] 0.80859722 1.57513188
##  [235,] 0.33319910 1.20255309
##  [236,] 1.06104275 0.79553710
##  [237,] 1.65761830 1.54580700
##  [238,] 2.81717761 0.49874151
##  [239,] 0.72116532 1.60560879
##  [240,] 1.79373477 0.75183984
##  [241,] 1.78794094 1.62531896
##  [242,] 1.40499474 0.40937853
##  [243,] 0.90682484 0.76557963
##  [244,] 0.82456792 0.50682554
##  [245,] 1.21038773 2.67295980
##  [246,] 0.71262583 0.41892444
##  [247,] 0.96837794 0.79538965
##  [248,] 0.74828411 0.56511754
##  [249,] 0.26613098 1.16471190
##  [250,] 0.67416492 1.26677981
##  [251,] 1.34772719 0.24940362
##  [252,] 1.04944414 1.25607200
##  [253,] 0.82298539 2.61308664
##  [254,] 1.51169270 1.25994156
##  [255,] 1.42520917 0.45462244
##  [256,] 1.96511159 1.09253144
##  [257,] 1.67490216 0.37807713
##  [258,] 1.25703910 0.77974194
##  [259,] 0.89221998 0.91252634
##  [260,] 1.08470362 0.89530226
##  [261,] 0.46255091 1.73872349
##  [262,] 0.73871530 0.99868395
##  [263,] 2.09512951 0.82889716
##  [264,] 0.60616111 0.34044063
##  [265,] 0.57517851 0.76724938
##  [266,] 0.40252286 0.33131919
##  [267,] 0.42210842 0.70401445
##  [268,] 0.53617254 1.15595519
##  [269,] 0.54430995 0.59134017
##  [270,] 0.55009343 0.97988292
##  [271,] 1.84143699 0.35328929
##  [272,] 0.51514846 0.87282408
##  [273,] 1.90134026 1.04269499
##  [274,] 0.25809104 0.19685097
##  [275,] 0.68932799 0.42169097
##  [276,] 0.40300648 0.52426694
##  [277,] 2.95665031 0.08391781
##  [278,] 0.60936911 0.45384567
##  [279,] 1.19775494 2.32101483
##  [280,] 0.82058551 0.90197078
##  [281,] 0.87736591 0.47963483
##  [282,] 1.37134703 0.32417650
##  [283,] 0.96802513 1.37089345
##  [284,] 1.24438964 2.14989445
##  [285,] 1.77221357 0.59720397
##  [286,] 0.85675930 0.82530658
##  [287,] 0.53313900 0.97310458
##  [288,] 1.04663198 1.73825848
##  [289,] 0.30282007 0.46098683
##  [290,] 0.60999339 0.75248397
##  [291,] 0.63877890 1.23344721
##  [292,] 1.34243792 2.55781516
##  [293,] 0.73298977 0.70657914
##  [294,] 0.83435431 1.29269100
##  [295,] 1.14791233 1.20189751
##  [296,] 1.06621341 1.37702436
##  [297,] 0.40692638 2.64764905
##  [298,] 0.72193822 0.61662276
##  [299,] 1.51687051 0.18042376
##  [300,] 0.95001961 0.29626051
##  [301,] 1.72433914 0.69503041
##  [302,] 0.27077079 0.75961416
##  [303,] 0.37079483 1.07612685
##  [304,] 1.08956313 1.12735565
##  [305,] 1.48228659 2.05160837
##  [306,] 0.75381539 1.38784228
##  [307,] 0.75893214 0.27046522
##  [308,] 1.12775834 1.01355167
##  [309,] 0.65910588 1.42214946
##  [310,] 1.04160228 1.02802744
##  [311,] 1.28544758 2.15868436
##  [312,] 1.55679585 3.52030644
##  [313,] 0.82082551 0.50298627
##  [314,] 0.75175706 0.93680713
##  [315,] 0.91914611 1.49142166
##  [316,] 1.22773247 1.09243551
##  [317,] 1.11308114 1.52185581
##  [318,] 0.38358077 0.83192976
##  [319,] 1.60040651 2.40158749
##  [320,] 1.71480808 0.32851417
##  [321,] 0.51616290 1.14166099
##  [322,] 0.70433689 0.92485550
##  [323,] 0.80410580 2.20331914
##  [324,] 1.52168573 0.85819999
##  [325,] 0.83199126 0.68466306
##  [326,] 0.56912855 1.21941954
##  [327,] 0.46324592 0.89747554
##  [328,] 0.96736915 0.78498603
##  [329,] 1.92614845 0.40884784
##  [330,] 0.65419465 0.22047028
##  [331,] 1.03694020 1.21182060
##  [332,] 3.29054340 1.14038989
##  [333,] 0.65962707 0.33033428
##  [334,] 0.28955657 1.97965170
##  [335,] 1.09577885 0.68652764
##  [336,] 1.50890793 1.66133626
##  [337,] 1.25254828 1.73150545
##  [338,] 0.55620358 0.59109242
##  [339,] 0.54949697 0.30124952
##  [340,] 1.26580072 0.90850080
##  [341,] 0.82348742 1.07597497
##  [342,] 0.68702225 1.13277005
##  [343,] 0.96111634 1.18428184
##  [344,] 1.05228129 0.74196942
##  [345,] 0.83546355 0.67597969
##  [346,] 1.31615505 1.23081194
##  [347,] 2.46225865 1.35143346
##  [348,] 0.71533250 0.60874214
##  [349,] 0.73692842 0.92060320
##  [350,] 0.42139887 1.11293505
##  [351,] 0.49389675 0.68761168
##  [352,] 1.51963159 0.75208900
##  [353,] 0.69726938 0.52497045
##  [354,] 0.62720929 1.00837073
##  [355,] 2.04995199 1.57162241
##  [356,] 1.27347679 1.29361262
##  [357,] 0.76274375 1.44103214
##  [358,] 1.34194129 1.23486560
##  [359,] 0.42688966 1.07077839
##  [360,] 0.46044927 4.28560664
##  [361,] 0.68039991 0.76846793
##  [362,] 0.77976454 1.26575369
##  [363,] 0.82146949 0.70420400
##  [364,] 0.30430246 0.68201993
##  [365,] 1.02866724 0.90858919
##  [366,] 1.13432947 1.05469563
##  [367,] 0.37746899 1.64290749
##  [368,] 1.02803601 0.48021224
##  [369,] 0.94199922 0.21488955
##  [370,] 1.02729999 0.47015321
##  [371,] 0.57517812 0.50682406
##  [372,] 0.49609623 0.80525178
##  [373,] 2.16867078 1.88172434
##  [374,] 0.92880416 3.97503533
##  [375,] 0.59180178 0.49299806
##  [376,] 1.76229505 1.88483611
##  [377,] 1.16659050 2.42951878
##  [378,] 1.79854748 0.51035536
##  [379,] 0.88770505 0.71943950
##  [380,] 0.56819848 0.96741733
##  [381,] 0.62016673 1.01421397
##  [382,] 0.79774914 0.85866127
##  [383,] 0.37697186 0.77271578
##  [384,] 1.58123092 3.33532562
##  [385,] 0.95772249 0.92062899
##  [386,] 1.45576652 1.59903138
##  [387,] 0.62533014 0.67185940
##  [388,] 0.83106282 0.87355780
##  [389,] 1.15832889 0.83753017
##  [390,] 2.98982270 2.05321088
##  [391,] 0.78620929 1.04365323
##  [392,] 1.54075868 2.46208217
##  [393,] 0.62366333 0.82309650
##  [394,] 1.02335524 1.25159512
##  [395,] 0.67273427 2.22766897
##  [396,] 0.63416814 0.59617772
##  [397,] 0.89315784 0.52481595
##  [398,] 0.77453405 1.18665577
##  [399,] 0.92699341 0.44545061
##  [400,] 1.34444701 0.45523466
##  [401,] 0.97100592 1.08883886
##  [402,] 0.42124430 0.52996063
##  [403,] 0.39156877 0.88542418
##  [404,] 1.18968041 1.31822257
##  [405,] 0.45389599 0.47211417
##  [406,] 1.08460403 0.29781088
##  [407,] 1.05310534 0.37018424
##  [408,] 0.87674843 1.96902906
##  [409,] 1.64426652 1.34441582
##  [410,] 2.02181257 0.97080627
##  [411,] 0.51453153 0.07748599
##  [412,] 0.63527466 1.70081409
##  [413,] 1.27477918 0.61713269
##  [414,] 0.89101875 0.87222499
##  [415,] 0.54975461 0.43430147
##  [416,] 0.74860443 0.94844450
##  [417,] 0.50643370 3.67574200
##  [418,] 1.00735813 0.85849798
##  [419,] 1.75829361 1.68146675
##  [420,] 0.76232804 0.95701389
##  [421,] 1.07494921 2.99489979
##  [422,] 0.20498613 1.16574669
##  [423,] 1.03105946 0.43391138
##  [424,] 0.62723957 2.42055080
##  [425,] 0.59015453 0.52122722
##  [426,] 1.15902482 0.71940146
##  [427,] 0.60657100 1.63655051
##  [428,] 0.53022225 1.25136223
##  [429,] 1.14132637 0.88576484
##  [430,] 0.79710900 1.17735361
##  [431,] 0.64926297 0.64768977
##  [432,] 0.65463873 0.38542813
##  [433,] 1.44905362 0.44109930
##  [434,] 0.21957411 0.62605485
##  [435,] 0.39795946 0.50775352
##  [436,] 1.66600272 0.52058561
##  [437,] 0.29678447 1.84805844
##  [438,] 1.08302967 0.60357107
##  [439,] 1.14360776 2.74326894
##  [440,] 2.07746531 0.42175959
##  [441,] 0.33066178 0.37912897
##  [442,] 2.10599808 1.03836883
##  [443,] 0.48655915 0.94393975
##  [444,] 1.65884576 0.34967215
##  [445,] 1.86990508 1.56730219
##  [446,] 0.82601897 0.66569474
##  [447,] 0.68206267 0.86747792
##  [448,] 0.85860012 0.68573465
##  [449,] 0.24114407 0.25064156
##  [450,] 1.38196407 1.89708526
##  [451,] 0.80223643 0.14407935
##  [452,] 0.44656945 1.17383017
##  [453,] 1.62566705 0.40384387
##  [454,] 0.76618314 0.99336909
##  [455,] 1.75808731 0.13769800
##  [456,] 0.59789880 1.65839253
##  [457,] 1.62747906 0.64521590
##  [458,] 1.68155567 0.86393375
##  [459,] 0.67728445 0.77032500
##  [460,] 1.11680599 1.21806425
##  [461,] 0.67236569 0.36999734
##  [462,] 0.73310382 0.90123501
##  [463,] 1.24148520 1.63207730
##  [464,] 0.99439326 0.29472170
##  [465,] 1.55631635 2.32379865
##  [466,] 0.82680722 1.35890454
##  [467,] 0.92328975 0.95816797
##  [468,] 0.18421425 1.60838768
##  [469,] 0.60512426 0.72839781
##  [470,] 0.28812962 1.24210891
##  [471,] 0.29209596 0.17606295
##  [472,] 2.00975502 1.11300511
##  [473,] 0.54707671 0.52718175
##  [474,] 1.18787163 1.98260313
##  [475,] 0.40171480 1.21873582
##  [476,] 1.14139310 1.04639585
##  [477,] 3.54195712 0.34914121
##  [478,] 0.76637816 1.40076612
##  [479,] 1.26981168 0.68104038
##  [480,] 1.80881386 2.73974288
##  [481,] 1.83245559 2.03945934
##  [482,] 0.48682388 0.71140216
##  [483,] 0.40165105 1.53682682
##  [484,] 1.17446792 0.50477838
##  [485,] 1.02633283 1.57352121
##  [486,] 0.46856407 0.52551024
##  [487,] 0.95515792 0.55646719
##  [488,] 0.90012463 0.87045905
##  [489,] 1.28605244 0.43625420
##  [490,] 1.23523805 0.89008855
##  [491,] 0.65078198 1.41471114
##  [492,] 0.61057532 0.60223530
##  [493,] 1.04959261 2.34786211
##  [494,] 0.62060588 0.88166805
##  [495,] 1.02080556 0.45586828
##  [496,] 0.87526029 0.94724643
##  [497,] 2.07904413 0.77797730
##  [498,] 0.71041741 0.66228561
##  [499,] 1.61097929 0.96726418
##  [500,] 0.72569718 0.25624738
##  [501,] 1.78026422 1.11704430
##  [502,] 0.72544646 2.52691984
##  [503,] 2.56602697 0.43464463
##  [504,] 0.50583360 0.77332288
##  [505,] 1.31753550 1.33470055
##  [506,] 0.54737726 1.47258330
##  [507,] 2.74801984 0.73153638
##  [508,] 0.56568793 0.55888669
##  [509,] 0.35421156 1.23260019
##  [510,] 1.31176860 0.40441918
##  [511,] 1.55616525 0.52981511
##  [512,] 2.23841988 1.48739482
##  [513,] 1.54127858 0.59273821
##  [514,] 0.70052481 0.76990835
##  [515,] 0.18170480 0.79628175
##  [516,] 0.90962752 1.69838725
##  [517,] 2.09454816 1.00983202
##  [518,] 0.48130666 1.23562116
##  [519,] 0.52084577 0.95478941
##  [520,] 0.41555666 1.52869425
##  [521,] 0.52290293 1.04121543
##  [522,] 1.05622403 2.31463313
##  [523,] 0.32841863 0.52698669
##  [524,] 1.65967821 2.42933742
##  [525,] 1.55552469 0.42456390
##  [526,] 1.40433575 1.50810668
##  [527,] 0.94681231 0.19913164
##  [528,] 0.56703549 0.50379741
##  [529,] 0.44284656 0.63411798
##  [530,] 0.57239047 0.66079588
##  [531,] 1.20449601 0.24918412
##  [532,] 2.31528364 2.42043538
##  [533,] 1.29501211 0.51812653
##  [534,] 0.87514509 3.45588316
##  [535,] 0.36180803 0.58716221
##  [536,] 0.85171790 0.38650864
##  [537,] 0.69688077 1.04712828
##  [538,] 2.12695024 0.43287366
##  [539,] 0.52600014 0.56944713
##  [540,] 1.34602675 0.59635642
##  [541,] 1.06791888 1.64563175
##  [542,] 0.42196874 0.73596188
##  [543,] 1.00126917 0.48329442
##  [544,] 0.60663778 1.39973911
##  [545,] 1.64880598 0.95452428
##  [546,] 1.28246160 1.05952435
##  [547,] 0.50719161 1.96481783
##  [548,] 0.40404715 0.70780117
##  [549,] 0.38662945 0.46032293
##  [550,] 1.88699782 0.75351137
##  [551,] 0.82060041 1.23814637
##  [552,] 0.88932103 1.32306961
##  [553,] 0.34740160 1.37850677
##  [554,] 0.90049824 0.30899471
##  [555,] 1.38021188 3.08812037
##  [556,] 2.68743240 0.61357943
##  [557,] 1.04592642 0.45472990
##  [558,] 0.86834193 0.43553519
##  [559,] 0.82859377 0.63924378
##  [560,] 1.14786015 2.70693296
##  [561,] 0.51392902 1.11518578
##  [562,] 0.65543988 1.44095985
##  [563,] 1.04068251 1.21603226
##  [564,] 0.55815766 0.93361593
##  [565,] 0.83522050 0.66375500
##  [566,] 0.72518270 0.70083522
##  [567,] 1.97730714 1.48353610
##  [568,] 0.61339377 0.60230637
##  [569,] 1.26272008 0.93715118
##  [570,] 1.30927414 0.46964080
##  [571,] 1.12699290 0.94772178
##  [572,] 1.24344421 0.55605700
##  [573,] 1.32496264 0.58104911
##  [574,] 0.56507243 0.65296869
##  [575,] 0.64703120 1.88770231
##  [576,] 1.85990798 1.43062866
##  [577,] 0.29135822 0.50589377
##  [578,] 0.64986406 1.01164589
##  [579,] 0.35484062 0.45316557
##  [580,] 1.35023328 1.10382884
##  [581,] 2.53154606 0.59371789
##  [582,] 1.13872609 0.47022346
##  [583,] 0.75917874 1.33239704
##  [584,] 0.49838294 0.15700202
##  [585,] 0.50866843 0.26193047
##  [586,] 3.15409376 1.72169043
##  [587,] 0.33734340 1.46991976
##  [588,] 0.56501898 0.77091406
##  [589,] 0.62293061 0.94963357
##  [590,] 1.67331708 0.22716815
##  [591,] 1.22901258 1.85624448
##  [592,] 0.66320444 1.46761900
##  [593,] 0.83659890 0.76134750
##  [594,] 0.64221536 1.32681412
##  [595,] 0.82885062 1.51809024
##  [596,] 0.79190090 0.25342829
##  [597,] 0.59775526 1.09395343
##  [598,] 0.80836624 1.54318415
##  [599,] 0.97715318 0.65017385
##  [600,] 1.75701552 1.21016212
##  [601,] 0.36316350 0.95085420
##  [602,] 1.55683907 2.09960702
##  [603,] 2.57388304 1.10716120
##  [604,] 1.55458935 0.92726058
##  [605,] 0.95762323 0.48343748
##  [606,] 1.76582874 0.97266205
##  [607,] 1.38883288 0.24989493
##  [608,] 0.65538037 0.45296996
##  [609,] 0.93617188 0.14064169
##  [610,] 2.12102062 0.57995649
##  [611,] 0.85948520 0.74929649
##  [612,] 0.99999627 0.52258887
##  [613,] 1.23820543 1.31490570
##  [614,] 0.63924282 1.03050087
##  [615,] 0.67133002 2.30115908
##  [616,] 0.48265403 0.97662108
##  [617,] 0.68073342 0.21136436
##  [618,] 0.73977290 0.55999427
##  [619,] 0.76800931 0.46928623
##  [620,] 0.39535526 1.49939201
##  [621,] 1.48679568 0.76102426
##  [622,] 0.61721593 1.20272311
##  [623,] 1.19699189 1.84908388
##  [624,] 0.50274933 1.01049000
##  [625,] 0.43793861 0.83525693
##  [626,] 1.17200106 2.01748224
##  [627,] 1.44000993 3.21440390
##  [628,] 0.62039356 1.14388505
##  [629,] 1.74483156 1.05502994
##  [630,] 2.49570535 1.24569365
##  [631,] 1.03912247 0.53174027
##  [632,] 0.98495924 1.41736741
##  [633,] 1.08827550 0.81431498
##  [634,] 0.53490192 0.73678205
##  [635,] 0.91127201 0.91874067
##  [636,] 1.42407021 0.72220187
##  [637,] 1.49694118 1.10970357
##  [638,] 0.58757881 0.44649731
##  [639,] 2.86295019 0.85863404
##  [640,] 0.35851694 0.15974849
##  [641,] 1.60288532 0.80306939
##  [642,] 1.56176456 2.11813896
##  [643,] 0.47495502 0.94881176
##  [644,] 0.72028651 0.82429600
##  [645,] 0.79139013 1.10597134
##  [646,] 0.99965111 1.30483872
##  [647,] 0.99625837 1.52340682
##  [648,] 0.76310790 0.25793294
##  [649,] 0.51070863 2.13162717
##  [650,] 1.24360316 0.42017577
##  [651,] 0.96870084 1.22298601
##  [652,] 2.27885280 1.90898342
##  [653,] 1.05019326 2.19135882
##  [654,] 1.16427785 1.32716642
##  [655,] 0.58942901 0.94165831
##  [656,] 1.10582776 1.83418872
##  [657,] 0.73623854 0.60238667
##  [658,] 0.61709768 1.30065636
##  [659,] 0.91720332 0.83020850
##  [660,] 0.68311778 1.76149403
##  [661,] 0.62515130 0.24972418
##  [662,] 1.12948872 1.54780284
##  [663,] 0.72948025 2.39477677
##  [664,] 1.00363150 1.10519312
##  [665,] 0.67785095 0.53700381
##  [666,] 1.28071466 1.41545439
##  [667,] 1.05884759 0.38852455
##  [668,] 1.11559977 0.64507027
##  [669,] 1.54020245 0.85886993
##  [670,] 1.82098332 2.42888064
##  [671,] 0.56625069 0.30361552
##  [672,] 0.77208519 0.90583939
##  [673,] 1.21402475 1.28865204
##  [674,] 0.39641304 0.77621175
##  [675,] 3.20023921 0.71468148
##  [676,] 0.33110363 0.36552667
##  [677,] 0.78569329 0.55287691
##  [678,] 1.03472093 1.68791736
##  [679,] 0.59981066 0.45145275
##  [680,] 1.25372313 0.42804081
##  [681,] 0.56130797 0.72349538
##  [682,] 0.97805016 0.20056039
##  [683,] 1.25755978 1.13303714
##  [684,] 1.55662155 1.26429428
##  [685,] 0.96482092 1.29290288
##  [686,] 1.97217397 1.23239968
##  [687,] 0.94561033 0.44491337
##  [688,] 1.46275076 1.45408908
##  [689,] 1.03040272 0.92012240
##  [690,] 0.66894744 0.33972767
##  [691,] 0.38996065 1.00943533
##  [692,] 1.70445191 2.18953498
##  [693,] 0.25890657 1.27993679
##  [694,] 1.57510328 0.70771356
##  [695,] 0.38976537 1.40515497
##  [696,] 0.48630517 0.90819073
##  [697,] 0.83565184 1.17059170
##  [698,] 0.81911199 0.68688753
##  [699,] 1.13914773 0.72122473
##  [700,] 0.91774625 0.85794496
##  [701,] 1.32802193 0.75795965
##  [702,] 0.20423270 0.77550499
##  [703,] 0.93960530 0.98696003
##  [704,] 0.36276554 0.86878709
##  [705,] 0.70620026 0.79261311
##  [706,] 0.57574611 1.86468542
##  [707,] 1.66142610 0.70539651
##  [708,] 0.74860791 0.09798201
##  [709,] 0.80474051 1.29801926
##  [710,] 1.53280980 0.50637859
##  [711,] 1.18312332 1.17360605
##  [712,] 0.43002511 0.89132148
##  [713,] 2.12352662 3.05631797
##  [714,] 0.57980470 0.37759132
##  [715,] 1.75552511 0.78894223
##  [716,] 0.93259410 3.69322613
##  [717,] 0.36282548 1.09037971
##  [718,] 1.07618058 1.12717943
##  [719,] 0.09873381 1.47479316
##  [720,] 0.80639479 1.57846277
##  [721,] 0.64202314 0.77588871
##  [722,] 1.70593230 0.27166893
##  [723,] 0.41079682 1.37298102
##  [724,] 1.50887501 0.66015466
##  [725,] 0.48972043 0.82139081
##  [726,] 0.64363274 0.46518961
##  [727,] 1.23352959 0.39086926
##  [728,] 0.55701108 1.25317866
##  [729,] 0.99842203 0.47665427
##  [730,] 1.74016927 1.59237041
##  [731,] 1.41894862 2.27528721
##  [732,] 1.11569411 0.44252006
##  [733,] 0.81052451 0.95353860
##  [734,] 0.20214119 0.86748258
##  [735,] 1.72941893 0.24300497
##  [736,] 0.52574987 0.51454899
##  [737,] 1.36674169 1.54263097
##  [738,] 1.74963943 0.81953420
##  [739,] 0.55730680 0.56058573
##  [740,] 1.19032826 0.65053090
##  [741,] 0.74447654 1.29622045
##  [742,] 0.77593508 2.22806557
##  [743,] 0.83900034 0.95949426
##  [744,] 0.48967800 0.85122919
##  [745,] 0.55341840 1.11601410
##  [746,] 0.37952663 0.57181675
##  [747,] 0.35991730 1.04841444
##  [748,] 0.72398703 1.37951468
##  [749,] 2.12874397 2.59414519
##  [750,] 1.01949123 0.35903435
##  [751,] 1.44901680 2.10592601
##  [752,] 1.03045023 1.73698673
##  [753,] 0.51249534 0.62207465
##  [754,] 1.43239337 0.40037155
##  [755,] 0.36635589 0.49800044
##  [756,] 0.74636218 1.19642822
##  [757,] 0.55851671 0.96172439
##  [758,] 2.12598738 0.46058575
##  [759,] 0.60522949 1.37722129
##  [760,] 0.30529500 0.99534790
##  [761,] 0.63965936 0.64070421
##  [762,] 0.65619947 0.61842989
##  [763,] 1.01091590 0.78601271
##  [764,] 1.40355326 1.19551393
##  [765,] 1.21564866 0.36679531
##  [766,] 0.85409101 0.29028972
##  [767,] 1.87726642 1.26043115
##  [768,] 1.16393399 0.66497737
##  [769,] 0.21856651 1.08196892
##  [770,] 1.60006921 2.14273829
##  [771,] 1.28916058 0.04489146
##  [772,] 1.09335096 0.77220704
##  [773,] 2.49754985 1.16411738
##  [774,] 0.84308850 0.48355307
##  [775,] 0.57610741 0.78298841
##  [776,] 0.91598832 2.16507791
##  [777,] 1.35549141 1.18090788
##  [778,] 1.68925770 0.35580667
##  [779,] 1.30420393 0.61024550
##  [780,] 0.81978560 0.85083482
##  [781,] 0.74751483 1.09551801
##  [782,] 0.44970679 1.65396479
##  [783,] 0.39228568 0.82957215
##  [784,] 0.96871531 0.75036342
##  [785,] 0.93696692 0.71354203
##  [786,] 1.16799486 1.45788091
##  [787,] 1.81254892 0.28249386
##  [788,] 0.78958134 1.07662551
##  [789,] 0.63510515 0.83158946
##  [790,] 1.62191388 0.17540344
##  [791,] 0.18880649 1.10094347
##  [792,] 0.90434415 2.87640494
##  [793,] 2.02162548 2.07006381
##  [794,] 0.73154752 2.69325674
##  [795,] 0.66898363 3.31618945
##  [796,] 1.06760855 1.16962276
##  [797,] 1.19640608 0.49451362
##  [798,] 2.03093366 0.56595644
##  [799,] 0.46572679 1.02368717
##  [800,] 1.05214980 0.82502482
##  [801,] 0.75721304 1.11724561
##  [802,] 0.61732702 0.50423426
##  [803,] 0.49308337 0.64500441
##  [804,] 1.25408350 1.01195523
##  [805,] 0.47457884 0.38967851
##  [806,] 1.67329616 1.37858203
##  [807,] 0.41814306 1.32049028
##  [808,] 1.19552488 1.45442785
##  [809,] 0.21608401 1.30952967
##  [810,] 0.90863959 1.27530502
##  [811,] 0.42266523 0.83902064
##  [812,] 0.89582300 1.18130488
##  [813,] 1.06570840 0.32106213
##  [814,] 0.47422793 0.79915133
##  [815,] 0.82334560 1.72449000
##  [816,] 2.07993735 2.80873061
##  [817,] 1.36532165 1.03099851
##  [818,] 0.74559791 0.30496085
##  [819,] 1.63101140 1.03084772
##  [820,] 1.82890042 0.49539438
##  [821,] 0.50167640 1.06949814
##  [822,] 1.10820422 0.39755712
##  [823,] 3.60113438 1.10231620
##  [824,] 0.61046498 0.59464982
##  [825,] 0.89618263 0.77303798
##  [826,] 0.19425026 0.88279368
##  [827,] 1.45581650 0.36851172
##  [828,] 1.34865693 1.20910735
##  [829,] 0.67997913 1.03510920
##  [830,] 0.15753690 1.13586975
##  [831,] 0.51815806 5.07060245
##  [832,] 0.86197224 1.57676229
##  [833,] 0.87016897 1.00143647
##  [834,] 2.60792495 0.71987487
##  [835,] 0.74851582 2.19897186
##  [836,] 0.58227823 1.74104118
##  [837,] 2.61944370 0.25804724
##  [838,] 1.10536478 0.58490870
##  [839,] 1.44789672 0.56159512
##  [840,] 1.85146414 0.33434120
##  [841,] 4.32862918 0.79598682
##  [842,] 0.51356999 0.80932130
##  [843,] 0.53675353 2.26169681
##  [844,] 0.77506538 0.68400654
##  [845,] 2.09512740 2.57188969
##  [846,] 1.12813599 0.65725054
##  [847,] 0.98388449 0.99286705
##  [848,] 1.92977156 0.13183909
##  [849,] 1.81233036 0.73670361
##  [850,] 1.24013624 0.57978796
##  [851,] 1.26103379 2.59185615
##  [852,] 0.88960938 0.79299764
##  [853,] 1.19369683 1.80585742
##  [854,] 0.90108683 0.41462820
##  [855,] 1.37684057 0.43769633
##  [856,] 0.98586972 1.30939148
##  [857,] 1.56996122 1.22117732
##  [858,] 4.05339997 0.18547662
##  [859,] 0.87797854 1.24297202
##  [860,] 0.74156459 0.99431027
##  [861,] 1.28273759 0.27834571
##  [862,] 1.11699922 1.01669600
##  [863,] 0.49927639 1.51761768
##  [864,] 0.87149275 0.77291511
##  [865,] 0.79636929 1.05217305
##  [866,] 0.54446958 1.88297141
##  [867,] 1.35669203 1.69329747
##  [868,] 0.79139083 1.48211506
##  [869,] 0.79306310 1.61663396
##  [870,] 0.53617231 0.72554824
##  [871,] 1.12053032 1.08157228
##  [872,] 0.52372152 0.53295293
##  [873,] 1.57528613 0.97943284
##  [874,] 0.53746371 1.32676842
##  [875,] 1.34409397 0.82262489
##  [876,] 0.58456068 0.80628174
##  [877,] 2.03928822 1.15999369
##  [878,] 0.61796567 0.24895819
##  [879,] 0.71766067 1.30729581
##  [880,] 1.11078495 1.58943801
##  [881,] 1.11081999 0.90947617
##  [882,] 1.62957992 1.08105628
##  [883,] 0.56514122 0.61113211
##  [884,] 0.69475297 1.16688313
##  [885,] 0.93984112 1.60451026
##  [886,] 0.81540812 1.24650606
##  [887,] 1.25182059 0.61782899
##  [888,] 0.23958564 0.69658715
##  [889,] 1.47641671 1.10066063
##  [890,] 0.64709202 1.45807253
##  [891,] 1.51184358 1.15058309
##  [892,] 2.49067817 1.79425208
##  [893,] 1.40989005 0.37502444
##  [894,] 0.80973226 1.31310551
##  [895,] 1.44701571 1.09180800
##  [896,] 1.94094805 1.29957325
##  [897,] 1.85784339 0.70181365
##  [898,] 0.83709056 0.70940252
##  [899,] 0.31536371 2.28069925
##  [900,] 0.25900435 1.93227606
##  [901,] 0.40748051 0.91489648
##  [902,] 0.65660895 1.12692690
##  [903,] 0.82204061 1.03226988
##  [904,] 1.32184354 1.24839304
##  [905,] 0.79338500 0.67727147
##  [906,] 0.66799530 1.32882336
##  [907,] 0.97292020 1.31288996
##  [908,] 1.48780221 1.81997846
##  [909,] 0.62510881 0.77756576
##  [910,] 0.46974454 0.48425767
##  [911,] 1.96184752 0.76192068
##  [912,] 0.73904997 2.04766600
##  [913,] 1.13426238 1.14507762
##  [914,] 0.70463026 1.05401694
##  [915,] 0.67563564 1.51736256
##  [916,] 1.31475657 2.20707052
##  [917,] 0.17390875 1.36192766
##  [918,] 1.48057759 0.92394053
##  [919,] 0.42456894 0.85636976
##  [920,] 2.27625767 1.26432540
##  [921,] 0.87810189 0.67755399
##  [922,] 1.06752545 1.06696469
##  [923,] 1.80887284 1.01632172
##  [924,] 1.16562092 0.85451967
##  [925,] 1.92696983 0.37374084
##  [926,] 1.34978578 0.96156596
##  [927,] 0.48427818 2.19574435
##  [928,] 1.14551138 2.08147284
##  [929,] 0.75736130 1.11200852
##  [930,] 1.19181157 0.11466078
##  [931,] 1.32423546 1.11946874
##  [932,] 0.78496369 0.87508383
##  [933,] 1.30178089 1.05190178
##  [934,] 0.98277570 0.57448775
##  [935,] 1.25859957 0.97205832
##  [936,] 1.86790922 0.68754858
##  [937,] 1.29904495 1.01102824
##  [938,] 1.26762078 0.87082992
##  [939,] 0.35466133 0.20543129
##  [940,] 0.49666318 3.45863482
##  [941,] 0.92049659 1.85276146
##  [942,] 1.18822034 1.64785113
##  [943,] 1.40636548 2.45177260
##  [944,] 0.77353591 1.25695198
##  [945,] 0.95456539 0.53531649
##  [946,] 1.35773434 3.00796492
##  [947,] 0.75712650 0.60279750
##  [948,] 1.16892249 1.21549117
##  [949,] 0.69261752 1.77061875
##  [950,] 0.62159658 1.03859306
##  [951,] 0.33106356 0.70290055
##  [952,] 0.39378655 0.31428341
##  [953,] 1.03706635 0.97503533
##  [954,] 0.77602159 0.51656912
##  [955,] 0.72052906 0.82563454
##  [956,] 0.63128675 1.81375383
##  [957,] 0.68872843 0.73182739
##  [958,] 0.75561275 0.23251083
##  [959,] 1.20591225 0.59770677
##  [960,] 1.58567718 1.15259835
##  [961,] 0.53854870 0.64593568
##  [962,] 0.41537872 0.64637812
##  [963,] 0.78061421 1.51401713
##  [964,] 0.18036516 2.13342315
##  [965,] 0.82719421 1.11190985
##  [966,] 0.24018955 0.99668062
##  [967,] 0.41488357 1.30866447
##  [968,] 1.64962595 0.97874453
##  [969,] 1.46045392 0.44362278
##  [970,] 0.66378657 0.68798715
##  [971,] 2.68774740 0.25696372
##  [972,] 1.68862370 0.70656443
##  [973,] 0.88655645 0.56525996
##  [974,] 1.04970407 0.49649119
##  [975,] 0.27050448 0.93403399
##  [976,] 0.91734601 0.49203899
##  [977,] 1.90310819 1.05225374
##  [978,] 0.18139904 0.96296166
##  [979,] 1.10237024 0.50461132
##  [980,] 0.86342220 1.47842263
##  [981,] 1.03320975 0.51547367
##  [982,] 0.50731128 1.06394333
##  [983,] 1.18967425 1.02081122
##  [984,] 1.83377541 0.99103334
##  [985,] 2.61949013 1.45993651
##  [986,] 1.01351881 1.03241760
##  [987,] 1.11880029 1.04859155
##  [988,] 0.36926907 1.44595535
##  [989,] 0.23511625 1.45019684
##  [990,] 1.49239145 0.50904018
##  [991,] 0.79617557 2.58323579
##  [992,] 0.92473477 0.50651023
##  [993,] 0.80655065 1.11536448
##  [994,] 0.85752977 1.04795680
##  [995,] 1.37434670 0.75244726
##  [996,] 0.80631858 0.36351018
##  [997,] 0.66350688 0.41710199
##  [998,] 0.45220216 1.93384597
##  [999,] 1.42647962 1.48353223
## 
## $model.matrix
##    (Intercept) microsite1 shrub_density
## 1            1          1            11
## 2            1          1            12
## 3            1         -1             0
## 4            1         -1             0
## 5            1          1            11
## 6            1          1            10
## 7            1         -1             0
## 8            1         -1             0
## 9            1          1            14
## 10           1          1            13
## 11           1         -1             0
## 12           1         -1             0
## 13           1          1            11
## 14           1          1            11
## 15           1         -1             0
## 16           1         -1             0
## 17           1          1            10
## 18           1          1            11
## 19           1          1            11
## 20           1          1            10
## 21           1         -1             0
## 22           1         -1             0
## 
## $terms
## pca_data ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data, microsite, shrub_density)
## attr(,"factors")
##               microsite shrub_density microsite:shrub_density
## pca_data              0             0                       0
## microsite             1             0                       1
## shrub_density         0             1                       1
## attr(,"term.labels")
## [1] "microsite"               "shrub_density"          
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## 
## attr(,"class")
## [1] "adonis"
dist <- vegdist(pca_data, species = "bray")
res <- pcoa(dist)
p1 <- as.data.frame(res$vectors)%>%
  dplyr::select(Axis.1, Axis.2) %>%
  bind_cols(env,.)

ggplot(p1, aes(Axis.1, Axis.2, group = microsite)) +
  geom_point(aes(color = microsite)) +
  geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
  scale_color_brewer(palette = "Set1") +
  labs(color = "", subtitle = "labels denote plot identity")

m02 <- betadisper(dist, env$microsite)
m02
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist, group = env$microsite)
## 
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
## 
## Average distance to median:
## Density    Open 
##  0.5063  0.4721 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m02)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## Groups     1 0.00638 0.006378  0.1212 0.7313
## Residuals 20 1.05203 0.052602
permutest(m02,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
## Groups     1 0.00638 0.006378 0.1212     99   0.71
## Residuals 20 1.05203 0.052602                     
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##         Density Open
## Density          0.7
## Open    0.73133
m02.HSD <- TukeyHSD(m02)
boxplot(m02)

m03 <- betadisper(dist, env$shrub_density)
m03
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist, group = env$shrub_density)
## 
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
## 
## Average distance to median:
##      0     10     11     12     13     14 
## 0.4721 0.5171 0.4968 0.0000 0.0000 0.0000 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m03)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq Mean Sq F value  Pr(>F)  
## Groups     5 0.61999 0.12400  2.3507 0.08827 .
## Residuals 16 0.84400 0.05275                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(m03,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq Mean Sq      F N.Perm Pr(>F)  
## Groups     5 0.61999 0.12400 2.3507     99   0.06 .
## Residuals 16 0.84400 0.05275                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##          0      10      11 12 13 14
## 0          0.71000 0.85000         
## 10 0.76968         0.87000         
## 11 0.84659 0.89212                 
## 12                                 
## 13                                 
## 14
m03.HSD <- TukeyHSD(m03)
boxplot(m03)

m04 <- betadisper(dist, env$site)
m04
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist, group = env$site)
## 
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
## 
## Average distance to median:
## Carrizo  Cuyama  Tecopa 
##  0.2828  0.3427  0.4625 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m04)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## Groups     2 0.11217 0.056087  1.1941 0.3247
## Residuals 19 0.89240 0.046969
permutest(m04,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
## Groups     2 0.11217 0.056087 1.1941     99   0.29
## Residuals 19 0.89240 0.046969                     
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##         Carrizo  Cuyama Tecopa
## Carrizo         0.52000   0.15
## Cuyama  0.54695           0.34
## Tecopa  0.19772 0.31784
m04.HSD <- TukeyHSD(m04)
boxplot(m04)

### 2023 Data

photo_2023 <- read.csv("observations_2023.csv")
summary(photo_2023)
##     region              site            site_code          microsite        
##  Length:192701      Length:192701      Length:192701      Length:192701     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       plot           cam_ID        month               date          
##  Min.   :1.000   Min.   :1.00   Length:192701      Length:192701     
##  1st Qu.:1.000   1st Qu.:1.00   Class :character   Class :character  
##  Median :2.000   Median :1.00   Mode  :character   Mode  :character  
##  Mean   :1.771   Mean   :1.49                                        
##  3rd Qu.:2.000   3rd Qu.:2.00                                        
##  Max.   :4.000   Max.   :2.00                                        
##       year      shrub_density         rep         identified_by     
##  Min.   :2023   Min.   : 0.000   Min.   :     1   Length:192701     
##  1st Qu.:2023   1st Qu.: 0.000   1st Qu.: 48176   Class :character  
##  Median :2023   Median :10.000   Median : 96351   Mode  :character  
##  Mean   :2023   Mean   : 6.239   Mean   : 96351                     
##  3rd Qu.:2023   3rd Qu.:11.000   3rd Qu.:144526                     
##  Max.   :2023   Max.   :14.000   Max.   :192701                     
##    filename          timestamp           animal.hit          class          
##  Length:192701      Length:192701      Min.   :0.000000   Length:192701     
##  Class :character   Class :character   1st Qu.:0.000000   Class :character  
##  Mode  :character   Mode  :character   Median :0.000000   Mode  :character  
##                                        Mean   :0.005392                     
##                                        3rd Qu.:0.000000                     
##                                        Max.   :1.000000                     
##     order              family             genus             species         
##  Length:192701      Length:192701      Length:192701      Length:192701     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##  common_name        number_of_objects
##  Length:192701      Min.   :1        
##  Class :character   1st Qu.:1        
##  Mode  :character   Median :1        
##                     Mean   :1        
##                     3rd Qu.:1        
##                     Max.   :2
photo_2023 <- photo_2023 %>%
  filter(common_name != "Human")
photo_2023 <- photo_2023 %>%
  filter(common_name != "Human-Camera Trapper")
photo_2023 <- photo_2023 %>%
  filter(common_name != "Domestic Dog")
photo_2023 <- photo_2023 %>%
  filter(common_name != "Vehicle")
photo_2023 <- photo_2023 %>%
  dplyr::filter(common_name != "Insect")
photo_2023 <- photo_2023 %>%
  dplyr::filter(common_name != "Animal")
photo_2023 <- photo_2023 %>%
  dplyr::filter(common_name != "Bird")
photo_2023 <- photo_2023 %>%
  dplyr::filter(common_name != "No CV Result")
  
count.hit_2023 <- photo_2023 %>%
  count(animal.hit) %>%
  na.omit()
summary(count.hit_2023)
##    animal.hit         n         
##  Min.   :0.00   Min.   :   546  
##  1st Qu.:0.25   1st Qu.: 48325  
##  Median :0.50   Median : 96104  
##  Mean   :0.50   Mean   : 96104  
##  3rd Qu.:0.75   3rd Qu.:143883  
##  Max.   :1.00   Max.   :191662
### 2023 had a 0.28% capture rate
### Animal Observations by Site_Code
animals_by_sitecode_2023 <- photo_2023%>%
  group_by(site_code, microsite, common_name) %>%
  summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode_2023 <- animals_by_sitecode_2023 %>%
  filter(common_name != "Blank") %>% filter(common_name != "No CV Result") 
### Animal observations by Site 2023
animals_by_site_2023 <- photo_2023 %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site_2023 <- animals_by_site_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result") 
### Animal observations by Density
animals_by_density_2023 <- photo_2023 %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density_2023 <- animals_by_density_2023 %>% filter(common_name != "Blank")  %>% filter(common_name != "No CV Result")
### Total Observations 2023
Total_Observations_2023 <- photo_2023 %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank")  %>% filter(common_name != "No CV Result")
density_obvs_2023 <- merge(animals_by_density_2023, Total_Observations_2023, all = TRUE)
density_obvs_2023$percent_presence <- density_obvs_2023$captures/density_obvs_2023$total
### Percent proportion Figure
plot2 <- ggplot(density_obvs_2023, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot2 + scale_fill_manual(values = c("#009900", "#0066cc"))

m2<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_2023)
anova(m2, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: total
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
## NULL                                     37     2413.4             
## microsite              1     89.8        36     2323.6   <2e-16 ***
## common_name           24   2323.6        12        0.0   <2e-16 ***
## microsite:common_name 12      0.0         0        0.0        1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e2 <- emmeans(m2, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
e2
## $emmeans
##  common_name                    emmean     SE  df asymp.LCL asymp.UCL
##  Black-tailed Jackrabbit          4.84 0.0630 Inf     4.713      4.96
##  Black-throated Sparrow         nonEst     NA  NA        NA        NA
##  Blunt-nosed Leopard Lizard     nonEst     NA  NA        NA        NA
##  Brewer's Blackbird               1.61 0.3162 Inf     0.990      2.23
##  California Ground Squirrel     nonEst     NA  NA        NA        NA
##  California Quail               nonEst     NA  NA        NA        NA
##  California Thrasher            nonEst     NA  NA        NA        NA
##  Common Raven                     3.30 0.1361 Inf     3.029      3.56
##  Coyote                           3.81 0.1054 Inf     3.600      4.01
##  Desert Cottontail              nonEst     NA  NA        NA        NA
##  Desert Iguana                  nonEst     NA  NA        NA        NA
##  Giant Kangaroo Rat               2.20 0.2357 Inf     1.735      2.66
##  Heermann's Kangaroo Rat          5.45 0.0464 Inf     5.356      5.54
##  Horned Lark                    nonEst     NA  NA        NA        NA
##  Kit Fox                          1.95 0.2673 Inf     1.422      2.47
##  Lizards and Snakes             nonEst     NA  NA        NA        NA
##  Loggerhead Shrike                1.61 0.3162 Inf     0.990      2.23
##  Mammal                         nonEst     NA  NA        NA        NA
##  Merriam's Kangaroo Rat           2.56 0.1961 Inf     2.181      2.95
##  Nelson's Antelope Squirrel       3.37 0.1313 Inf     3.110      3.62
##  Salinas Pocket Mouse             1.10 0.4082 Inf     0.298      1.90
##  Say's Phoebe                     1.39 0.3536 Inf     0.693      2.08
##  Vesper Sparrow                 nonEst     NA  NA        NA        NA
##  Western whiptail               nonEst     NA  NA        NA        NA
##  White-tailed Antelope Squirrel   1.79 0.2887 Inf     1.226      2.36
## 
## Results are averaged over the levels of: microsite 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                        estimate
##  (Black-tailed Jackrabbit) - (Black-throated Sparrow)              nonEst
##  (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard)          nonEst
##  (Black-tailed Jackrabbit) - Brewer's Blackbird                    3.2268
##  (Black-tailed Jackrabbit) - California Ground Squirrel            nonEst
##  (Black-tailed Jackrabbit) - California Quail                      nonEst
##  (Black-tailed Jackrabbit) - California Thrasher                   nonEst
##  (Black-tailed Jackrabbit) - Common Raven                          1.5404
##  (Black-tailed Jackrabbit) - Coyote                                1.0296
##  (Black-tailed Jackrabbit) - Desert Cottontail                     nonEst
##  (Black-tailed Jackrabbit) - Desert Iguana                         nonEst
##  (Black-tailed Jackrabbit) - Giant Kangaroo Rat                    2.6391
##  (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat              -0.6105
##  (Black-tailed Jackrabbit) - Horned Lark                           nonEst
##  (Black-tailed Jackrabbit) - Kit Fox                               2.8904
##  (Black-tailed Jackrabbit) - Lizards and Snakes                    nonEst
##  (Black-tailed Jackrabbit) - Loggerhead Shrike                     3.2268
##  (Black-tailed Jackrabbit) - Mammal                                nonEst
##  (Black-tailed Jackrabbit) - Merriam's Kangaroo Rat                2.2713
##  (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel            1.4690
##  (Black-tailed Jackrabbit) - Salinas Pocket Mouse                  3.7377
##  (Black-tailed Jackrabbit) - Say's Phoebe                          3.4500
##  (Black-tailed Jackrabbit) - Vesper Sparrow                        nonEst
##  (Black-tailed Jackrabbit) - Western whiptail                      nonEst
##  (Black-tailed Jackrabbit) - (White-tailed Antelope Squirrel)      3.0445
##  (Black-throated Sparrow) - (Blunt-nosed Leopard Lizard)           nonEst
##  (Black-throated Sparrow) - Brewer's Blackbird                     nonEst
##  (Black-throated Sparrow) - California Ground Squirrel             nonEst
##  (Black-throated Sparrow) - California Quail                       nonEst
##  (Black-throated Sparrow) - California Thrasher                    nonEst
##  (Black-throated Sparrow) - Common Raven                           nonEst
##  (Black-throated Sparrow) - Coyote                                 nonEst
##  (Black-throated Sparrow) - Desert Cottontail                      nonEst
##  (Black-throated Sparrow) - Desert Iguana                          nonEst
##  (Black-throated Sparrow) - Giant Kangaroo Rat                     nonEst
##  (Black-throated Sparrow) - Heermann's Kangaroo Rat                nonEst
##  (Black-throated Sparrow) - Horned Lark                            nonEst
##  (Black-throated Sparrow) - Kit Fox                                nonEst
##  (Black-throated Sparrow) - Lizards and Snakes                     nonEst
##  (Black-throated Sparrow) - Loggerhead Shrike                      nonEst
##  (Black-throated Sparrow) - Mammal                                 nonEst
##  (Black-throated Sparrow) - Merriam's Kangaroo Rat                 nonEst
##  (Black-throated Sparrow) - Nelson's Antelope Squirrel             nonEst
##  (Black-throated Sparrow) - Salinas Pocket Mouse                   nonEst
##  (Black-throated Sparrow) - Say's Phoebe                           nonEst
##  (Black-throated Sparrow) - Vesper Sparrow                         nonEst
##  (Black-throated Sparrow) - Western whiptail                       nonEst
##  (Black-throated Sparrow) - (White-tailed Antelope Squirrel)       nonEst
##  (Blunt-nosed Leopard Lizard) - Brewer's Blackbird                 nonEst
##  (Blunt-nosed Leopard Lizard) - California Ground Squirrel         nonEst
##  (Blunt-nosed Leopard Lizard) - California Quail                   nonEst
##  (Blunt-nosed Leopard Lizard) - California Thrasher                nonEst
##  (Blunt-nosed Leopard Lizard) - Common Raven                       nonEst
##  (Blunt-nosed Leopard Lizard) - Coyote                             nonEst
##  (Blunt-nosed Leopard Lizard) - Desert Cottontail                  nonEst
##  (Blunt-nosed Leopard Lizard) - Desert Iguana                      nonEst
##  (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat                 nonEst
##  (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat            nonEst
##  (Blunt-nosed Leopard Lizard) - Horned Lark                        nonEst
##  (Blunt-nosed Leopard Lizard) - Kit Fox                            nonEst
##  (Blunt-nosed Leopard Lizard) - Lizards and Snakes                 nonEst
##  (Blunt-nosed Leopard Lizard) - Loggerhead Shrike                  nonEst
##  (Blunt-nosed Leopard Lizard) - Mammal                             nonEst
##  (Blunt-nosed Leopard Lizard) - Merriam's Kangaroo Rat             nonEst
##  (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel         nonEst
##  (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse               nonEst
##  (Blunt-nosed Leopard Lizard) - Say's Phoebe                       nonEst
##  (Blunt-nosed Leopard Lizard) - Vesper Sparrow                     nonEst
##  (Blunt-nosed Leopard Lizard) - Western whiptail                   nonEst
##  (Blunt-nosed Leopard Lizard) - (White-tailed Antelope Squirrel)   nonEst
##  Brewer's Blackbird - California Ground Squirrel                   nonEst
##  Brewer's Blackbird - California Quail                             nonEst
##  Brewer's Blackbird - California Thrasher                          nonEst
##  Brewer's Blackbird - Common Raven                                -1.6864
##  Brewer's Blackbird - Coyote                                      -2.1972
##  Brewer's Blackbird - Desert Cottontail                            nonEst
##  Brewer's Blackbird - Desert Iguana                                nonEst
##  Brewer's Blackbird - Giant Kangaroo Rat                          -0.5878
##  Brewer's Blackbird - Heermann's Kangaroo Rat                     -3.8373
##  Brewer's Blackbird - Horned Lark                                  nonEst
##  Brewer's Blackbird - Kit Fox                                     -0.3365
##  Brewer's Blackbird - Lizards and Snakes                           nonEst
##  Brewer's Blackbird - Loggerhead Shrike                            0.0000
##  Brewer's Blackbird - Mammal                                       nonEst
##  Brewer's Blackbird - Merriam's Kangaroo Rat                      -0.9555
##  Brewer's Blackbird - Nelson's Antelope Squirrel                  -1.7579
##  Brewer's Blackbird - Salinas Pocket Mouse                         0.5108
##  Brewer's Blackbird - Say's Phoebe                                 0.2231
##  Brewer's Blackbird - Vesper Sparrow                               nonEst
##  Brewer's Blackbird - Western whiptail                             nonEst
##  Brewer's Blackbird - (White-tailed Antelope Squirrel)            -0.1823
##  California Ground Squirrel - California Quail                     nonEst
##  California Ground Squirrel - California Thrasher                  nonEst
##  California Ground Squirrel - Common Raven                         nonEst
##  California Ground Squirrel - Coyote                               nonEst
##  California Ground Squirrel - Desert Cottontail                    nonEst
##  California Ground Squirrel - Desert Iguana                        nonEst
##  California Ground Squirrel - Giant Kangaroo Rat                   nonEst
##  California Ground Squirrel - Heermann's Kangaroo Rat              nonEst
##  California Ground Squirrel - Horned Lark                          nonEst
##  California Ground Squirrel - Kit Fox                              nonEst
##  California Ground Squirrel - Lizards and Snakes                   nonEst
##  California Ground Squirrel - Loggerhead Shrike                    nonEst
##  California Ground Squirrel - Mammal                               nonEst
##  California Ground Squirrel - Merriam's Kangaroo Rat               nonEst
##  California Ground Squirrel - Nelson's Antelope Squirrel           nonEst
##  California Ground Squirrel - Salinas Pocket Mouse                 nonEst
##  California Ground Squirrel - Say's Phoebe                         nonEst
##  California Ground Squirrel - Vesper Sparrow                       nonEst
##  California Ground Squirrel - Western whiptail                     nonEst
##  California Ground Squirrel - (White-tailed Antelope Squirrel)     nonEst
##  California Quail - California Thrasher                            nonEst
##  California Quail - Common Raven                                   nonEst
##  California Quail - Coyote                                         nonEst
##  California Quail - Desert Cottontail                              nonEst
##  California Quail - Desert Iguana                                  nonEst
##  California Quail - Giant Kangaroo Rat                             nonEst
##  California Quail - Heermann's Kangaroo Rat                        nonEst
##  California Quail - Horned Lark                                    nonEst
##  California Quail - Kit Fox                                        nonEst
##  California Quail - Lizards and Snakes                             nonEst
##  California Quail - Loggerhead Shrike                              nonEst
##  California Quail - Mammal                                         nonEst
##  California Quail - Merriam's Kangaroo Rat                         nonEst
##  California Quail - Nelson's Antelope Squirrel                     nonEst
##  California Quail - Salinas Pocket Mouse                           nonEst
##  California Quail - Say's Phoebe                                   nonEst
##  California Quail - Vesper Sparrow                                 nonEst
##  California Quail - Western whiptail                               nonEst
##  California Quail - (White-tailed Antelope Squirrel)               nonEst
##  California Thrasher - Common Raven                                nonEst
##  California Thrasher - Coyote                                      nonEst
##  California Thrasher - Desert Cottontail                           nonEst
##  California Thrasher - Desert Iguana                               nonEst
##  California Thrasher - Giant Kangaroo Rat                          nonEst
##  California Thrasher - Heermann's Kangaroo Rat                     nonEst
##  California Thrasher - Horned Lark                                 nonEst
##  California Thrasher - Kit Fox                                     nonEst
##  California Thrasher - Lizards and Snakes                          nonEst
##  California Thrasher - Loggerhead Shrike                           nonEst
##  California Thrasher - Mammal                                      nonEst
##  California Thrasher - Merriam's Kangaroo Rat                      nonEst
##  California Thrasher - Nelson's Antelope Squirrel                  nonEst
##  California Thrasher - Salinas Pocket Mouse                        nonEst
##  California Thrasher - Say's Phoebe                                nonEst
##  California Thrasher - Vesper Sparrow                              nonEst
##  California Thrasher - Western whiptail                            nonEst
##  California Thrasher - (White-tailed Antelope Squirrel)            nonEst
##  Common Raven - Coyote                                            -0.5108
##  Common Raven - Desert Cottontail                                  nonEst
##  Common Raven - Desert Iguana                                      nonEst
##  Common Raven - Giant Kangaroo Rat                                 1.0986
##  Common Raven - Heermann's Kangaroo Rat                           -2.1509
##  Common Raven - Horned Lark                                        nonEst
##  Common Raven - Kit Fox                                            1.3499
##  Common Raven - Lizards and Snakes                                 nonEst
##  Common Raven - Loggerhead Shrike                                  1.6864
##  Common Raven - Mammal                                             nonEst
##  Common Raven - Merriam's Kangaroo Rat                             0.7309
##  Common Raven - Nelson's Antelope Squirrel                        -0.0715
##  Common Raven - Salinas Pocket Mouse                               2.1972
##  Common Raven - Say's Phoebe                                       1.9095
##  Common Raven - Vesper Sparrow                                     nonEst
##  Common Raven - Western whiptail                                   nonEst
##  Common Raven - (White-tailed Antelope Squirrel)                   1.5041
##  Coyote - Desert Cottontail                                        nonEst
##  Coyote - Desert Iguana                                            nonEst
##  Coyote - Giant Kangaroo Rat                                       1.6094
##  Coyote - Heermann's Kangaroo Rat                                 -1.6401
##  Coyote - Horned Lark                                              nonEst
##  Coyote - Kit Fox                                                  1.8608
##  Coyote - Lizards and Snakes                                       nonEst
##  Coyote - Loggerhead Shrike                                        2.1972
##  Coyote - Mammal                                                   nonEst
##  Coyote - Merriam's Kangaroo Rat                                   1.2417
##  Coyote - Nelson's Antelope Squirrel                               0.4394
##  Coyote - Salinas Pocket Mouse                                     2.7081
##  Coyote - Say's Phoebe                                             2.4204
##  Coyote - Vesper Sparrow                                           nonEst
##  Coyote - Western whiptail                                         nonEst
##  Coyote - (White-tailed Antelope Squirrel)                         2.0149
##  Desert Cottontail - Desert Iguana                                 nonEst
##  Desert Cottontail - Giant Kangaroo Rat                            nonEst
##  Desert Cottontail - Heermann's Kangaroo Rat                       nonEst
##  Desert Cottontail - Horned Lark                                   nonEst
##  Desert Cottontail - Kit Fox                                       nonEst
##  Desert Cottontail - Lizards and Snakes                            nonEst
##  Desert Cottontail - Loggerhead Shrike                             nonEst
##  Desert Cottontail - Mammal                                        nonEst
##  Desert Cottontail - Merriam's Kangaroo Rat                        nonEst
##  Desert Cottontail - Nelson's Antelope Squirrel                    nonEst
##  Desert Cottontail - Salinas Pocket Mouse                          nonEst
##  Desert Cottontail - Say's Phoebe                                  nonEst
##  Desert Cottontail - Vesper Sparrow                                nonEst
##  Desert Cottontail - Western whiptail                              nonEst
##  Desert Cottontail - (White-tailed Antelope Squirrel)              nonEst
##  Desert Iguana - Giant Kangaroo Rat                                nonEst
##  Desert Iguana - Heermann's Kangaroo Rat                           nonEst
##  Desert Iguana - Horned Lark                                       nonEst
##  Desert Iguana - Kit Fox                                           nonEst
##  Desert Iguana - Lizards and Snakes                                nonEst
##  Desert Iguana - Loggerhead Shrike                                 nonEst
##  Desert Iguana - Mammal                                            nonEst
##  Desert Iguana - Merriam's Kangaroo Rat                            nonEst
##  Desert Iguana - Nelson's Antelope Squirrel                        nonEst
##  Desert Iguana - Salinas Pocket Mouse                              nonEst
##  Desert Iguana - Say's Phoebe                                      nonEst
##  Desert Iguana - Vesper Sparrow                                    nonEst
##  Desert Iguana - Western whiptail                                  nonEst
##  Desert Iguana - (White-tailed Antelope Squirrel)                  nonEst
##  Giant Kangaroo Rat - Heermann's Kangaroo Rat                     -3.2495
##  Giant Kangaroo Rat - Horned Lark                                  nonEst
##  Giant Kangaroo Rat - Kit Fox                                      0.2513
##  Giant Kangaroo Rat - Lizards and Snakes                           nonEst
##  Giant Kangaroo Rat - Loggerhead Shrike                            0.5878
##  Giant Kangaroo Rat - Mammal                                       nonEst
##  Giant Kangaroo Rat - Merriam's Kangaroo Rat                      -0.3677
##  Giant Kangaroo Rat - Nelson's Antelope Squirrel                  -1.1701
##  Giant Kangaroo Rat - Salinas Pocket Mouse                         1.0986
##  Giant Kangaroo Rat - Say's Phoebe                                 0.8109
##  Giant Kangaroo Rat - Vesper Sparrow                               nonEst
##  Giant Kangaroo Rat - Western whiptail                             nonEst
##  Giant Kangaroo Rat - (White-tailed Antelope Squirrel)             0.4055
##  Heermann's Kangaroo Rat - Horned Lark                             nonEst
##  Heermann's Kangaroo Rat - Kit Fox                                 3.5008
##  Heermann's Kangaroo Rat - Lizards and Snakes                      nonEst
##  Heermann's Kangaroo Rat - Loggerhead Shrike                       3.8373
##  Heermann's Kangaroo Rat - Mammal                                  nonEst
##  Heermann's Kangaroo Rat - Merriam's Kangaroo Rat                  2.8818
##  Heermann's Kangaroo Rat - Nelson's Antelope Squirrel              2.0794
##  Heermann's Kangaroo Rat - Salinas Pocket Mouse                    4.3481
##  Heermann's Kangaroo Rat - Say's Phoebe                            4.0604
##  Heermann's Kangaroo Rat - Vesper Sparrow                          nonEst
##  Heermann's Kangaroo Rat - Western whiptail                        nonEst
##  Heermann's Kangaroo Rat - (White-tailed Antelope Squirrel)        3.6550
##  Horned Lark - Kit Fox                                             nonEst
##  Horned Lark - Lizards and Snakes                                  nonEst
##  Horned Lark - Loggerhead Shrike                                   nonEst
##  Horned Lark - Mammal                                              nonEst
##  Horned Lark - Merriam's Kangaroo Rat                              nonEst
##  Horned Lark - Nelson's Antelope Squirrel                          nonEst
##  Horned Lark - Salinas Pocket Mouse                                nonEst
##  Horned Lark - Say's Phoebe                                        nonEst
##  Horned Lark - Vesper Sparrow                                      nonEst
##  Horned Lark - Western whiptail                                    nonEst
##  Horned Lark - (White-tailed Antelope Squirrel)                    nonEst
##  Kit Fox - Lizards and Snakes                                      nonEst
##  Kit Fox - Loggerhead Shrike                                       0.3365
##  Kit Fox - Mammal                                                  nonEst
##  Kit Fox - Merriam's Kangaroo Rat                                 -0.6190
##  Kit Fox - Nelson's Antelope Squirrel                             -1.4214
##  Kit Fox - Salinas Pocket Mouse                                    0.8473
##  Kit Fox - Say's Phoebe                                            0.5596
##  Kit Fox - Vesper Sparrow                                          nonEst
##  Kit Fox - Western whiptail                                        nonEst
##  Kit Fox - (White-tailed Antelope Squirrel)                        0.1542
##  Lizards and Snakes - Loggerhead Shrike                            nonEst
##  Lizards and Snakes - Mammal                                       nonEst
##  Lizards and Snakes - Merriam's Kangaroo Rat                       nonEst
##  Lizards and Snakes - Nelson's Antelope Squirrel                   nonEst
##  Lizards and Snakes - Salinas Pocket Mouse                         nonEst
##  Lizards and Snakes - Say's Phoebe                                 nonEst
##  Lizards and Snakes - Vesper Sparrow                               nonEst
##  Lizards and Snakes - Western whiptail                             nonEst
##  Lizards and Snakes - (White-tailed Antelope Squirrel)             nonEst
##  Loggerhead Shrike - Mammal                                        nonEst
##  Loggerhead Shrike - Merriam's Kangaroo Rat                       -0.9555
##  Loggerhead Shrike - Nelson's Antelope Squirrel                   -1.7579
##  Loggerhead Shrike - Salinas Pocket Mouse                          0.5108
##  Loggerhead Shrike - Say's Phoebe                                  0.2231
##  Loggerhead Shrike - Vesper Sparrow                                nonEst
##  Loggerhead Shrike - Western whiptail                              nonEst
##  Loggerhead Shrike - (White-tailed Antelope Squirrel)             -0.1823
##  Mammal - Merriam's Kangaroo Rat                                   nonEst
##  Mammal - Nelson's Antelope Squirrel                               nonEst
##  Mammal - Salinas Pocket Mouse                                     nonEst
##  Mammal - Say's Phoebe                                             nonEst
##  Mammal - Vesper Sparrow                                           nonEst
##  Mammal - Western whiptail                                         nonEst
##  Mammal - (White-tailed Antelope Squirrel)                         nonEst
##  Merriam's Kangaroo Rat - Nelson's Antelope Squirrel              -0.8023
##  Merriam's Kangaroo Rat - Salinas Pocket Mouse                     1.4663
##  Merriam's Kangaroo Rat - Say's Phoebe                             1.1787
##  Merriam's Kangaroo Rat - Vesper Sparrow                           nonEst
##  Merriam's Kangaroo Rat - Western whiptail                         nonEst
##  Merriam's Kangaroo Rat - (White-tailed Antelope Squirrel)         0.7732
##  Nelson's Antelope Squirrel - Salinas Pocket Mouse                 2.2687
##  Nelson's Antelope Squirrel - Say's Phoebe                         1.9810
##  Nelson's Antelope Squirrel - Vesper Sparrow                       nonEst
##  Nelson's Antelope Squirrel - Western whiptail                     nonEst
##  Nelson's Antelope Squirrel - (White-tailed Antelope Squirrel)     1.5755
##  Salinas Pocket Mouse - Say's Phoebe                              -0.2877
##  Salinas Pocket Mouse - Vesper Sparrow                             nonEst
##  Salinas Pocket Mouse - Western whiptail                           nonEst
##  Salinas Pocket Mouse - (White-tailed Antelope Squirrel)          -0.6931
##  Say's Phoebe - Vesper Sparrow                                     nonEst
##  Say's Phoebe - Western whiptail                                   nonEst
##  Say's Phoebe - (White-tailed Antelope Squirrel)                  -0.4055
##  Vesper Sparrow - Western whiptail                                 nonEst
##  Vesper Sparrow - (White-tailed Antelope Squirrel)                 nonEst
##  Western whiptail - (White-tailed Antelope Squirrel)               nonEst
##      SE  df z.ratio p.value
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3224 Inf  10.008  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.1500 Inf  10.273  <.0001
##  0.1228 Inf   8.385  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2440 Inf  10.817  <.0001
##  0.0783 Inf  -7.801  <.0001
##      NA  NA      NA      NA
##  0.2746 Inf  10.526  <.0001
##      NA  NA      NA      NA
##  0.3224 Inf  10.008  <.0001
##      NA  NA      NA      NA
##  0.2060 Inf  11.027  <.0001
##  0.1456 Inf  10.087  <.0001
##  0.4131 Inf   9.048  <.0001
##  0.3591 Inf   9.607  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2955 Inf  10.304  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3443 Inf  -4.899  0.0003
##  0.3333 Inf  -6.592  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3944 Inf  -1.490  0.9991
##  0.3196 Inf -12.006  <.0001
##      NA  NA      NA      NA
##  0.4140 Inf  -0.813  1.0000
##      NA  NA      NA      NA
##  0.4472 Inf   0.000  1.0000
##      NA  NA      NA      NA
##  0.3721 Inf  -2.568  0.6434
##  0.3424 Inf  -5.134  0.0001
##  0.5164 Inf   0.989  1.0000
##  0.4743 Inf   0.470  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.4282 Inf  -0.426  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.1721 Inf  -2.968  0.3313
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2722 Inf   4.037  0.0127
##  0.1438 Inf -14.959  <.0001
##      NA  NA      NA      NA
##  0.2999 Inf   4.501  0.0018
##      NA  NA      NA      NA
##  0.3443 Inf   4.899  0.0003
##      NA  NA      NA      NA
##  0.2387 Inf   3.062  0.2698
##  0.1891 Inf  -0.378  1.0000
##  0.4303 Inf   5.106  0.0001
##  0.3788 Inf   5.041  0.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3191 Inf   4.713  0.0007
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2582 Inf   6.233  <.0001
##  0.1152 Inf -14.239  <.0001
##      NA  NA      NA      NA
##  0.2873 Inf   6.477  <.0001
##      NA  NA      NA      NA
##  0.3333 Inf   6.592  <.0001
##      NA  NA      NA      NA
##  0.2226 Inf   5.577  <.0001
##  0.1684 Inf   2.609  0.6102
##  0.4216 Inf   6.423  <.0001
##  0.3689 Inf   6.560  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3073 Inf   6.556  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2402 Inf -13.527  <.0001
##      NA  NA      NA      NA
##  0.3563 Inf   0.705  1.0000
##      NA  NA      NA      NA
##  0.3944 Inf   1.490  0.9991
##      NA  NA      NA      NA
##  0.3066 Inf  -1.199  1.0000
##  0.2698 Inf  -4.337  0.0037
##  0.4714 Inf   2.331  0.8137
##  0.4249 Inf   1.908  0.9731
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3727 Inf   1.088  1.0000
##      NA  NA      NA      NA
##  0.2713 Inf  12.906  <.0001
##      NA  NA      NA      NA
##  0.3196 Inf  12.006  <.0001
##      NA  NA      NA      NA
##  0.2015 Inf  14.299  <.0001
##  0.1393 Inf  14.931  <.0001
##  0.4109 Inf  10.582  <.0001
##  0.3566 Inf  11.387  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2924 Inf  12.501  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.4140 Inf   0.813  1.0000
##      NA  NA      NA      NA
##  0.3315 Inf  -1.867  0.9792
##  0.2978 Inf  -4.773  0.0005
##  0.4879 Inf   1.736  0.9917
##  0.4432 Inf   1.263  0.9999
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3934 Inf   0.392  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3721 Inf  -2.568  0.6434
##  0.3424 Inf  -5.134  0.0001
##  0.5164 Inf   0.989  1.0000
##  0.4743 Inf   0.470  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.4282 Inf  -0.426  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2360 Inf  -3.400  0.1122
##  0.4529 Inf   3.238  0.1755
##  0.4043 Inf   2.915  0.3684
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3490 Inf   2.216  0.8770
##  0.4288 Inf   5.290  <.0001
##  0.3771 Inf   5.253  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3171 Inf   4.968  0.0002
##  0.5401 Inf  -0.533  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.5000 Inf  -1.386  0.9997
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.4564 Inf  -0.888  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
## 
## Results are averaged over the levels of: microsite 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 25 estimates
animals_density_2023 <- photo_2023 %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density_2023 <- animals_density_2023 %>% filter(common_name != "Blank")
pca_data_2023 <- animals_density_2023 ### Created new df for pcoa data
pca_data_2023 <- pca_data_2023 %>%
  spread(common_name, captures) %>%
  ungroup() %>%
  dplyr::select(-site_code, -microsite, -plot) %>%
  replace(is.na(.),0)
dim(pca_data_2023)
## [1] 23 26
env_2023 <- read.csv("environment_2023.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)
## [1] 22  5
model01 <- adonis(pca_data_2023 ~ microsite*shrub_density, data = env_2023)
## 'adonis' will be deprecated: use 'adonis2' instead
model01
## $aov.tab
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##               Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)
## microsite      1    0.4511 0.45114  1.6082 0.06930  0.150
## shrub_density  1    0.4482 0.44823  1.5978 0.06885  0.151
## Residuals     20    5.6105 0.28052         0.86184       
## Total         22    6.5099                 1.00000       
## 
## $call
## adonis(formula = pca_data_2023 ~ microsite * shrub_density, data = env_2023)
## 
## $coefficients
##                          shrub_density Black-tailed Jackrabbit
## (Intercept)                 -10.398601               2.8438228
## microsite1                  -14.216783              -3.7925408
## shrub_density                 2.876923               0.4769231
## microsite1:shrub_density            NA                      NA
##                          Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept)                        4.545455e-02                 0.12820513
## microsite1                        -4.545455e-02                 0.12820513
## shrub_density                     -6.331379e-18                -0.01538462
## microsite1:shrub_density                     NA                         NA
##                          Brewer's Blackbird California Ground Squirrel
## (Intercept)                      -2.5571096                -0.21794872
## microsite1                       -2.6480186                -0.21794872
## shrub_density                     0.4923077                 0.04615385
## microsite1:shrub_density                 NA                         NA
##                          California Quail California Thrasher Common Raven
## (Intercept)                    -1.8205128          0.47435897    2.6340326
## microsite1                     -1.8205128          0.47435897    1.7249417
## shrub_density                   0.3384615         -0.07692308   -0.2615385
## microsite1:shrub_density               NA                  NA           NA
##                             Coyote Desert Cottontail Desert Iguana
## (Intercept)               3.060606        -2.3391608  4.545455e-02
## microsite1                1.606061        -2.4300699 -4.545455e-02
## shrub_density            -0.200000         0.4461538  8.356568e-18
## microsite1:shrub_density        NA                NA            NA
##                          Giant Kangaroo Rat Heermann's Kangaroo Rat Horned Lark
## (Intercept)                      0.03263403               -7.849650  0.38461538
## microsite1                      -0.05827506              -15.304196  0.38461538
## shrub_density                    0.06153846                3.169231 -0.04615385
## microsite1:shrub_density                 NA                      NA          NA
##                             Kit Fox Lizards and Snakes Loggerhead Shrike
## (Intercept)               2.1165501       2.727273e-01        -0.4801865
## microsite1                1.9347319      -2.727273e-01        -0.5710956
## shrub_density            -0.3230769      -1.513076e-17         0.1230769
## microsite1:shrub_density         NA                 NA                NA
##                               Mammal Merriam's Kangaroo Rat
## (Intercept)              -0.21794872             1.10256410
## microsite1               -0.21794872             0.10256410
## shrub_density             0.04615385            -0.09230769
## microsite1:shrub_density          NA                     NA
##                          Nelson's Antelope Squirrel Salinas Pocket Mouse
## (Intercept)                               0.3694639          -0.39044289
## microsite1                               -0.2668998          -0.48135198
## shrub_density                             0.1538462           0.09230769
## microsite1:shrub_density                         NA                   NA
##                          Say's Phoebe Vesper Sparrow Western whiptail
## (Intercept)               0.264568765      1.5384615       0.12820513
## microsite1               -0.008158508      1.5384615       0.12820513
## shrub_density            -0.015384615     -0.1846154      -0.01538462
## microsite1:shrub_density           NA             NA               NA
##                          White-tailed Antelope Squirrel
## (Intercept)                                  0.35547786
## microsite1                                  -0.09906760
## shrub_density                               -0.01538462
## microsite1:shrub_density                             NA
## 
## $coef.sites
##                                    1           2           3           4
## (Intercept)               1.08713373  1.19242360  1.04021622  0.53368864
## microsite1                0.27702067  0.36812565  0.28650205 -0.08921598
## shrub_density            -0.05750503 -0.07418322 -0.03323392  0.02592824
## microsite1:shrub_density          NA          NA          NA          NA
##                                    5            6          7           8
## (Intercept)               1.09169046  0.725232927  0.9149049  0.73036046
## microsite1                0.43517627 -0.003397368  0.1285966 -0.03557956
## shrub_density            -0.08696283 -0.012927632 -0.0247195  0.01666357
## microsite1:shrub_density          NA           NA         NA          NA
##                                   9         10          11          12
## (Intercept)               1.3003752  1.4998669  0.80294716  0.64129703
## microsite1                0.6190745  0.7936762  0.05159731 -0.03324735
## shrub_density            -0.1227435 -0.1491194 -0.01154984  0.01898538
## microsite1:shrub_density         NA         NA          NA          NA
##                                   13          14          15          16
## (Intercept)               0.99239018  0.66809163  0.63858697 -0.08681298
## microsite1                0.34039631 -0.04876636 -0.21003546 -0.87304773
## shrub_density            -0.06950611 -0.01035118  0.04254088  0.14042524
## microsite1:shrub_density          NA          NA          NA          NA
##                                   17         18          19          20
## (Intercept)               0.17811261  0.1694050 -0.01538676  0.91285079
## microsite1               -0.64882951 -0.6891926 -0.81989153  0.22319732
## shrub_density             0.09926822  0.1072032  0.13358216 -0.04137692
## microsite1:shrub_density          NA         NA          NA          NA
##                                   21          22         23
## (Intercept)               0.95345668  1.13091299  0.9134932
## microsite1                0.30456994  0.48161788  0.2756840
## shrub_density            -0.05633216 -0.08893335 -0.0522899
## microsite1:shrub_density          NA          NA         NA
## 
## $f.perms
##               [,1]       [,2]
##    [1,] 0.46530682 1.02723751
##    [2,] 0.88426933 0.78604776
##    [3,] 0.70620154 0.63080175
##    [4,] 0.78452045 0.95628203
##    [5,] 0.65625235 1.82310508
##    [6,] 0.41204149 0.54038847
##    [7,] 0.89333851 1.00396992
##    [8,] 0.41256690 1.48955056
##    [9,] 0.48282021 1.02010885
##   [10,] 1.14187511 2.00075243
##   [11,] 0.36229674 1.56766319
##   [12,] 0.58029315 0.42205591
##   [13,] 0.58982293 1.60395896
##   [14,] 0.40710734 0.77769454
##   [15,] 0.95333565 1.88883713
##   [16,] 1.89469529 1.59088448
##   [17,] 2.45304109 0.63421638
##   [18,] 0.69630316 1.34923615
##   [19,] 0.80457920 0.77456754
##   [20,] 0.15063732 0.69008846
##   [21,] 1.13696558 0.42212376
##   [22,] 0.57269079 0.84454886
##   [23,] 1.18934103 1.11533190
##   [24,] 0.83650477 0.90631662
##   [25,] 0.57862928 1.42057363
##   [26,] 0.61307193 0.62928553
##   [27,] 0.50921759 1.41689070
##   [28,] 0.60546656 0.64746795
##   [29,] 1.25270446 0.48835053
##   [30,] 1.26307206 2.38667203
##   [31,] 0.23235340 1.61597370
##   [32,] 0.25519945 0.33154146
##   [33,] 0.75109231 0.42832222
##   [34,] 1.06972304 0.71389876
##   [35,] 1.07312335 1.24024031
##   [36,] 0.83765182 1.14596365
##   [37,] 0.23608720 1.38144786
##   [38,] 0.23646657 0.48009064
##   [39,] 0.58964491 0.37062061
##   [40,] 0.48456003 0.90619211
##   [41,] 0.65961997 1.24739487
##   [42,] 1.25104421 1.45741741
##   [43,] 0.56564461 0.54844614
##   [44,] 0.25032223 0.77210011
##   [45,] 0.95109709 1.22318596
##   [46,] 1.46420310 1.11263496
##   [47,] 0.51714262 0.62612509
##   [48,] 2.76190601 0.70446752
##   [49,] 1.18936147 0.16901212
##   [50,] 1.64382681 0.48552713
##   [51,] 3.23377952 1.82092259
##   [52,] 0.24945196 1.69121480
##   [53,] 1.27956261 0.95120022
##   [54,] 0.96948035 1.08715516
##   [55,] 1.59823567 1.08603772
##   [56,] 1.04950411 0.91346544
##   [57,] 1.01897396 0.45499685
##   [58,] 1.15766496 0.78100845
##   [59,] 1.01565455 0.69805486
##   [60,] 2.31711393 0.72780980
##   [61,] 0.44804372 1.71442977
##   [62,] 0.79397276 0.69967097
##   [63,] 1.39942111 1.26609972
##   [64,] 1.68970019 1.23760441
##   [65,] 0.91464806 1.36589037
##   [66,] 2.53903862 1.60905130
##   [67,] 0.61575439 0.49280490
##   [68,] 0.41482495 2.18819519
##   [69,] 0.83117127 0.46837486
##   [70,] 0.76577886 2.13667526
##   [71,] 0.51625655 0.48058234
##   [72,] 1.46860717 1.05988007
##   [73,] 0.30255506 0.40426702
##   [74,] 1.29760873 1.56555964
##   [75,] 1.41998330 3.24075853
##   [76,] 1.24260780 0.92310513
##   [77,] 1.80652569 2.14665450
##   [78,] 1.91744283 0.85202856
##   [79,] 1.43946643 0.77570236
##   [80,] 0.77806136 0.51128660
##   [81,] 0.71479324 0.73011698
##   [82,] 0.53680056 0.85101026
##   [83,] 1.07005151 1.11684791
##   [84,] 1.71588906 1.34747867
##   [85,] 0.76941961 1.16405303
##   [86,] 0.63630776 0.91394931
##   [87,] 0.91248219 0.98393008
##   [88,] 1.00228284 0.67300761
##   [89,] 0.65131253 1.75229157
##   [90,] 0.77968516 1.52923439
##   [91,] 1.71683735 1.69846664
##   [92,] 0.61588691 1.52385656
##   [93,] 1.05041722 1.15447018
##   [94,] 0.93976045 0.70670096
##   [95,] 0.64232490 0.82854646
##   [96,] 0.70109004 0.53484380
##   [97,] 1.04227843 1.28127132
##   [98,] 0.62201413 1.04581002
##   [99,] 0.36807410 0.87355216
##  [100,] 1.24064707 1.27973479
##  [101,] 0.36739973 2.13175783
##  [102,] 0.48306130 0.95525639
##  [103,] 0.81294670 1.34078557
##  [104,] 0.95304735 1.73826827
##  [105,] 0.86857800 1.21959915
##  [106,] 0.40632261 0.69965638
##  [107,] 0.86728796 0.58104252
##  [108,] 1.41292956 1.05578068
##  [109,] 0.53976593 0.41886274
##  [110,] 0.91660335 1.50396749
##  [111,] 1.89117847 0.99368072
##  [112,] 0.81332130 1.02296833
##  [113,] 1.47576577 0.66995709
##  [114,] 0.77460486 0.91077866
##  [115,] 0.99770113 0.56513033
##  [116,] 0.98447887 0.88613353
##  [117,] 0.51129048 0.91977763
##  [118,] 0.98669305 2.48616172
##  [119,] 1.62157610 1.26525289
##  [120,] 0.93181053 1.43058869
##  [121,] 1.18864718 0.68280866
##  [122,] 0.53111471 2.19039419
##  [123,] 1.94351497 1.11313360
##  [124,] 0.13414627 1.85826877
##  [125,] 1.33452938 0.86654021
##  [126,] 1.65249616 0.37831285
##  [127,] 0.83755390 1.37761772
##  [128,] 1.50556135 1.10949992
##  [129,] 0.88938730 1.62272497
##  [130,] 0.56183307 1.70675602
##  [131,] 0.73083010 1.31921036
##  [132,] 1.17423834 2.44633501
##  [133,] 0.49782917 0.45386517
##  [134,] 0.47657091 1.28589391
##  [135,] 0.75200720 1.43174276
##  [136,] 1.00913713 3.24713773
##  [137,] 1.09243827 0.81319995
##  [138,] 1.40825521 1.18850727
##  [139,] 0.69582573 1.00753389
##  [140,] 0.93527522 2.06205516
##  [141,] 0.60930843 0.38837708
##  [142,] 1.26095197 1.63794420
##  [143,] 1.20242140 1.38930442
##  [144,] 0.46777829 1.30466432
##  [145,] 1.79928052 1.36143803
##  [146,] 0.52201200 1.26196633
##  [147,] 0.92161132 1.15659157
##  [148,] 1.11684031 0.39135684
##  [149,] 0.72622248 1.36395308
##  [150,] 0.51163800 1.17418613
##  [151,] 2.02050677 1.93491881
##  [152,] 2.75265758 1.69491729
##  [153,] 1.16829912 1.73368808
##  [154,] 0.70251715 0.59188409
##  [155,] 0.50824134 0.68701208
##  [156,] 1.83428348 0.44537161
##  [157,] 1.55958705 1.48104197
##  [158,] 0.87137331 0.44441998
##  [159,] 0.98540066 1.04006703
##  [160,] 0.64364022 0.97581283
##  [161,] 0.32909581 1.39167411
##  [162,] 2.05990174 2.12633028
##  [163,] 1.07255914 1.80083008
##  [164,] 1.39788039 1.25723004
##  [165,] 2.14211229 0.69573215
##  [166,] 0.54414691 0.84363510
##  [167,] 1.82794380 2.20311860
##  [168,] 1.23042487 1.32745948
##  [169,] 0.66844433 1.07058347
##  [170,] 0.83984098 0.45841726
##  [171,] 0.70076220 0.88839508
##  [172,] 0.57886695 0.60981158
##  [173,] 0.76004397 0.90969644
##  [174,] 1.34257837 1.09095278
##  [175,] 0.57381778 1.72115098
##  [176,] 0.70737063 0.90565490
##  [177,] 1.06482290 0.93910026
##  [178,] 2.00828175 0.63893664
##  [179,] 0.40013174 1.50895199
##  [180,] 1.21813551 1.65409718
##  [181,] 0.90186247 0.41548422
##  [182,] 0.42251282 0.72029939
##  [183,] 1.27949673 1.21985913
##  [184,] 1.52206229 1.05129101
##  [185,] 0.88868450 1.11253361
##  [186,] 0.27949835 1.45173308
##  [187,] 0.94198928 0.33061383
##  [188,] 0.49606202 1.07958765
##  [189,] 1.55663285 0.49993530
##  [190,] 0.85455453 0.34557739
##  [191,] 0.96487380 1.29793929
##  [192,] 1.65445145 1.84665853
##  [193,] 1.11098788 0.66271960
##  [194,] 0.87041731 0.63957031
##  [195,] 0.66760746 1.47997546
##  [196,] 0.66560114 0.53447970
##  [197,] 0.71200113 1.02318158
##  [198,] 0.70367066 0.33632519
##  [199,] 2.71660719 1.81886444
##  [200,] 0.74691285 0.49110802
##  [201,] 0.30103464 0.70357336
##  [202,] 0.78327949 1.55348509
##  [203,] 1.37772334 0.87399868
##  [204,] 1.95543865 0.68288126
##  [205,] 0.77387786 1.17054965
##  [206,] 1.75522668 0.97407892
##  [207,] 1.28849346 0.79074527
##  [208,] 1.04858328 1.14006317
##  [209,] 1.38259599 0.41257835
##  [210,] 1.25800193 0.96097196
##  [211,] 0.53722954 1.13412658
##  [212,] 0.76084481 0.96122596
##  [213,] 1.08406523 1.06944877
##  [214,] 0.66478212 0.92955945
##  [215,] 1.14399399 1.00121488
##  [216,] 0.67169760 0.67117778
##  [217,] 0.58884603 0.57924801
##  [218,] 1.84274594 1.62425236
##  [219,] 1.42065302 0.51100036
##  [220,] 0.54931118 1.79362298
##  [221,] 0.49343252 1.01033924
##  [222,] 1.16382618 0.82247626
##  [223,] 0.58572000 0.80228356
##  [224,] 0.77194482 1.18508892
##  [225,] 3.03843729 1.19597257
##  [226,] 1.28824066 0.85717100
##  [227,] 1.93086773 2.07343619
##  [228,] 1.54333170 1.80324181
##  [229,] 0.46177968 0.88294422
##  [230,] 0.57727315 0.56198887
##  [231,] 0.85779276 1.20881861
##  [232,] 1.74246235 0.77493753
##  [233,] 0.89064864 1.07117332
##  [234,] 3.39855196 0.64585340
##  [235,] 2.19430429 0.69914624
##  [236,] 0.99540729 0.39336771
##  [237,] 2.58908873 0.44138581
##  [238,] 0.35905719 0.31655864
##  [239,] 0.66168071 0.99287647
##  [240,] 2.12578691 0.50881799
##  [241,] 0.57358727 1.08865141
##  [242,] 0.60157872 2.75099387
##  [243,] 0.93961432 0.38170589
##  [244,] 1.53163044 0.90700258
##  [245,] 0.85721071 1.08690777
##  [246,] 1.53078070 0.66524922
##  [247,] 1.00367672 0.87379937
##  [248,] 1.03671711 1.35062214
##  [249,] 0.95078428 0.72492618
##  [250,] 0.77757221 1.20733014
##  [251,] 0.69646531 0.98328585
##  [252,] 0.54167935 0.98817772
##  [253,] 0.91450682 0.35274037
##  [254,] 0.28636514 1.90832255
##  [255,] 0.41354135 1.49257191
##  [256,] 1.24254963 0.60553741
##  [257,] 0.97577600 2.22481644
##  [258,] 0.77708379 1.30804286
##  [259,] 0.80046292 0.78418576
##  [260,] 1.91563857 0.31955859
##  [261,] 0.75130378 0.07262442
##  [262,] 0.62025098 0.31996274
##  [263,] 1.31011357 0.69571487
##  [264,] 0.37633799 1.83843331
##  [265,] 0.72110436 0.74762501
##  [266,] 1.10922897 0.59321359
##  [267,] 1.40052235 1.83625895
##  [268,] 0.91145502 0.68624908
##  [269,] 1.29684060 1.21255829
##  [270,] 0.62106951 1.71606099
##  [271,] 0.66972124 1.60828643
##  [272,] 1.01889127 0.85638632
##  [273,] 0.42175103 1.19553012
##  [274,] 0.67058203 1.01442465
##  [275,] 2.52533082 0.84107598
##  [276,] 1.06385864 0.90993279
##  [277,] 1.51749637 1.59968364
##  [278,] 0.64845669 0.54590064
##  [279,] 1.10208444 0.71323201
##  [280,] 0.46322610 0.30627824
##  [281,] 0.57236329 1.49264254
##  [282,] 1.37909826 0.07157821
##  [283,] 0.51797181 1.39246476
##  [284,] 1.07796604 0.58171822
##  [285,] 1.78415828 1.03930263
##  [286,] 0.50706414 0.82902199
##  [287,] 1.19960424 0.56430012
##  [288,] 2.13528173 1.54812720
##  [289,] 0.80455349 1.20175721
##  [290,] 0.91592213 0.97958968
##  [291,] 0.60963299 1.38386943
##  [292,] 1.37232870 1.64875112
##  [293,] 0.48970779 1.18371934
##  [294,] 2.13407573 0.62765483
##  [295,] 1.64479381 2.37694090
##  [296,] 1.19969301 1.24729495
##  [297,] 2.17518176 0.88529303
##  [298,] 1.42980211 1.64953895
##  [299,] 0.80011632 0.95622167
##  [300,] 0.72438753 0.64805834
##  [301,] 1.44836822 0.49478082
##  [302,] 2.38547500 1.02163196
##  [303,] 0.65222793 0.46724732
##  [304,] 1.03647239 1.87407226
##  [305,] 1.82202668 1.52545811
##  [306,] 0.79010247 0.63241906
##  [307,] 0.69700611 1.42728965
##  [308,] 0.70199248 0.78271593
##  [309,] 0.40426735 0.51812330
##  [310,] 0.77126264 0.33042731
##  [311,] 2.45913810 1.31303611
##  [312,] 0.46745239 1.20446379
##  [313,] 0.55607217 0.71823225
##  [314,] 0.67332900 0.54343125
##  [315,] 1.38229328 1.29640146
##  [316,] 1.42645097 0.26366943
##  [317,] 0.60510221 0.62097231
##  [318,] 0.91563832 1.00015286
##  [319,] 1.21435845 0.42227639
##  [320,] 1.32152486 0.77688942
##  [321,] 1.45640537 1.64735934
##  [322,] 0.50841608 0.16139729
##  [323,] 0.56043721 0.24493033
##  [324,] 1.37889741 1.45756898
##  [325,] 0.91384127 0.98391988
##  [326,] 0.81392436 2.23013229
##  [327,] 0.82807832 0.56237698
##  [328,] 1.10166081 0.96834243
##  [329,] 1.58292481 1.49520460
##  [330,] 0.37662122 1.52129685
##  [331,] 0.89300582 1.19523649
##  [332,] 0.84560598 0.29121073
##  [333,] 2.25785004 1.01681017
##  [334,] 0.47269119 1.42113289
##  [335,] 0.88196630 1.19019127
##  [336,] 1.49632386 1.92747739
##  [337,] 2.77998162 1.42693252
##  [338,] 0.28840349 0.52713450
##  [339,] 0.92067133 1.73577314
##  [340,] 1.00804286 1.26603823
##  [341,] 1.11246170 1.69728670
##  [342,] 0.91133494 1.16587038
##  [343,] 1.76445589 0.53811682
##  [344,] 1.27925321 0.46152030
##  [345,] 0.52825138 0.60843543
##  [346,] 1.70997835 1.96168680
##  [347,] 1.03923684 1.14776435
##  [348,] 0.40502762 0.89708071
##  [349,] 1.28350973 1.23013715
##  [350,] 1.25564540 0.42229789
##  [351,] 1.64258227 1.09171404
##  [352,] 0.92118758 0.56748410
##  [353,] 0.86040032 0.81863130
##  [354,] 1.69899572 0.85490511
##  [355,] 1.22596355 1.79188674
##  [356,] 0.71469190 1.45335000
##  [357,] 2.80540830 1.39858704
##  [358,] 2.86840302 0.81154718
##  [359,] 0.28171695 0.84701828
##  [360,] 0.92875620 0.96019518
##  [361,] 0.77666180 1.43979798
##  [362,] 1.77694007 0.83408601
##  [363,] 1.23991085 0.91210485
##  [364,] 0.76604896 1.32249074
##  [365,] 0.60024124 0.12059228
##  [366,] 1.08187259 0.73623924
##  [367,] 2.92321521 0.29742093
##  [368,] 0.72660121 1.19351945
##  [369,] 0.34425760 1.83367118
##  [370,] 0.35538003 2.13729145
##  [371,] 0.83995701 1.00523904
##  [372,] 1.55806804 0.53720524
##  [373,] 0.23208583 1.69158297
##  [374,] 1.32888888 0.62780927
##  [375,] 1.33015852 0.25612282
##  [376,] 2.01560849 1.09767665
##  [377,] 0.70254963 1.48837277
##  [378,] 2.07068218 0.25140776
##  [379,] 2.23828743 1.25234220
##  [380,] 1.52702640 1.26963750
##  [381,] 1.04599784 1.55395520
##  [382,] 0.98173056 0.52918565
##  [383,] 1.12644235 0.77091087
##  [384,] 0.47249533 1.02493022
##  [385,] 0.70300630 1.37855371
##  [386,] 0.45133351 0.65320519
##  [387,] 0.87310279 1.05575329
##  [388,] 1.71396186 2.20005523
##  [389,] 1.03085621 0.30756693
##  [390,] 0.82115052 1.32512309
##  [391,] 0.51298535 1.11216001
##  [392,] 1.22773049 1.02291168
##  [393,] 0.71928070 0.79206802
##  [394,] 0.88513582 0.48610556
##  [395,] 0.60650896 1.92161069
##  [396,] 0.32687116 1.34095685
##  [397,] 0.98494295 0.79868640
##  [398,] 0.31600148 0.84668829
##  [399,] 1.54294007 2.58940897
##  [400,] 0.89588448 1.01550098
##  [401,] 2.28994111 0.65747664
##  [402,] 4.41191444 0.22401333
##  [403,] 2.73143490 0.72450080
##  [404,] 0.87697012 1.25030646
##  [405,] 0.45216677 0.76694316
##  [406,] 0.70067514 0.80051945
##  [407,] 0.75979745 0.23896808
##  [408,] 0.27000233 0.75627706
##  [409,] 0.95617993 1.12102579
##  [410,] 0.62499418 0.37919900
##  [411,] 1.19684170 1.64327055
##  [412,] 2.78319061 0.42623559
##  [413,] 0.56950238 0.44949989
##  [414,] 1.74633602 1.00657830
##  [415,] 2.35666491 1.22441415
##  [416,] 0.16590799 0.30864236
##  [417,] 1.16484818 1.43991777
##  [418,] 0.14696948 0.70919240
##  [419,] 0.63264480 1.65807751
##  [420,] 1.77994860 1.99301919
##  [421,] 0.86365838 2.69104384
##  [422,] 0.28507649 1.28688735
##  [423,] 0.18101255 1.64793665
##  [424,] 1.63531146 1.66592224
##  [425,] 0.40162653 0.50776011
##  [426,] 0.82068211 1.23753455
##  [427,] 0.70313432 3.02318829
##  [428,] 0.47086593 1.14565366
##  [429,] 0.78714267 0.36614885
##  [430,] 0.62561546 1.36641342
##  [431,] 1.90555025 0.89136650
##  [432,] 0.67337634 0.13314496
##  [433,] 0.56207331 0.25761564
##  [434,] 0.72778845 0.98278122
##  [435,] 0.88895133 0.69566003
##  [436,] 0.93942277 1.13326425
##  [437,] 0.84383700 0.45213874
##  [438,] 0.66707673 1.05790886
##  [439,] 1.90780761 0.59883314
##  [440,] 0.54188714 1.31729211
##  [441,] 0.91323614 0.30014274
##  [442,] 0.37357132 1.46881595
##  [443,] 1.03320292 1.09180192
##  [444,] 0.37417062 0.19169805
##  [445,] 0.76510285 0.59544493
##  [446,] 1.45121809 0.91073683
##  [447,] 1.09856577 0.79492290
##  [448,] 1.18696285 2.61183520
##  [449,] 0.71628968 1.11678650
##  [450,] 2.41690443 1.53018450
##  [451,] 0.50376716 0.54596686
##  [452,] 1.23721379 0.35256826
##  [453,] 1.11616603 0.42227063
##  [454,] 0.73835076 0.66583788
##  [455,] 0.99972562 0.70327182
##  [456,] 1.47044175 1.99405551
##  [457,] 0.45858427 0.74675260
##  [458,] 2.04814710 0.82251696
##  [459,] 0.90411216 0.78114508
##  [460,] 1.04298560 0.45584395
##  [461,] 0.60788382 0.87641161
##  [462,] 1.04704938 0.69966783
##  [463,] 0.45008053 0.98270338
##  [464,] 0.55624734 0.53650210
##  [465,] 1.12663784 2.23036221
##  [466,] 0.25767619 0.76784483
##  [467,] 0.11154732 0.80741817
##  [468,] 1.05579222 0.79517963
##  [469,] 0.30576819 1.46541962
##  [470,] 0.58643384 0.57697974
##  [471,] 0.63282140 0.88275589
##  [472,] 1.69641191 0.88984517
##  [473,] 1.26717993 2.15163482
##  [474,] 1.10516341 0.37598780
##  [475,] 1.12399190 0.44119726
##  [476,] 1.26130020 1.22436258
##  [477,] 0.56804638 0.77268792
##  [478,] 1.80909834 1.41945298
##  [479,] 0.82277439 1.65438904
##  [480,] 0.50178543 1.54936044
##  [481,] 0.81502051 1.01937551
##  [482,] 1.42383757 2.29273047
##  [483,] 0.49734544 0.55751165
##  [484,] 0.47659967 1.86007195
##  [485,] 0.73075056 0.60484229
##  [486,] 0.17494451 1.27270791
##  [487,] 1.74769338 1.57171402
##  [488,] 1.28571703 1.02132470
##  [489,] 0.65603780 0.55651703
##  [490,] 1.26904784 1.05863526
##  [491,] 1.80205559 1.16863148
##  [492,] 0.76203519 0.56957529
##  [493,] 1.71534101 2.46182630
##  [494,] 0.81541606 1.20958633
##  [495,] 1.68260093 0.91081364
##  [496,] 0.96018824 0.80250587
##  [497,] 0.87491248 0.88146856
##  [498,] 0.86699009 0.84679673
##  [499,] 1.16663417 0.98938932
##  [500,] 1.50670022 0.79339340
##  [501,] 1.08882393 0.20229786
##  [502,] 1.34060678 1.32269829
##  [503,] 1.64373385 1.23044231
##  [504,] 1.13339030 1.32501154
##  [505,] 0.44164934 0.44883522
##  [506,] 0.65104301 0.36332243
##  [507,] 1.65350427 1.47538026
##  [508,] 1.07421347 1.59135889
##  [509,] 0.66086247 2.33672469
##  [510,] 0.58632752 1.99962425
##  [511,] 0.26637763 1.24418891
##  [512,] 1.21853375 0.70356831
##  [513,] 0.32538953 1.49292646
##  [514,] 1.33692127 0.86178500
##  [515,] 2.81626496 1.20190845
##  [516,] 0.42683567 1.12698261
##  [517,] 0.53704919 0.87752195
##  [518,] 0.56979497 1.05929277
##  [519,] 1.18231474 2.07642426
##  [520,] 1.66419676 0.56564426
##  [521,] 0.92953837 1.15432756
##  [522,] 0.67020900 0.75030154
##  [523,] 0.74534706 0.45097931
##  [524,] 0.54135387 0.72944945
##  [525,] 2.35947635 0.62157709
##  [526,] 0.16511604 1.33049329
##  [527,] 1.41231635 2.17425979
##  [528,] 2.17124925 2.09428628
##  [529,] 1.04973121 1.70561948
##  [530,] 0.92325348 0.56117506
##  [531,] 2.52168888 0.68363758
##  [532,] 0.75941498 1.12350138
##  [533,] 0.74541266 0.54046150
##  [534,] 0.74350808 0.32508951
##  [535,] 1.23163565 0.63380639
##  [536,] 0.47128293 0.06440537
##  [537,] 0.89301416 0.95596221
##  [538,] 1.18978120 1.03389319
##  [539,] 0.58226293 2.68670556
##  [540,] 1.76510447 0.69766858
##  [541,] 1.46501459 0.69640242
##  [542,] 0.45229471 1.13887401
##  [543,] 3.18681960 1.39411796
##  [544,] 0.35133083 1.33235866
##  [545,] 1.00100489 0.64685776
##  [546,] 1.32577038 1.14147262
##  [547,] 0.63507532 1.80892575
##  [548,] 2.66040905 0.91707794
##  [549,] 1.83567893 1.13265916
##  [550,] 0.98671065 1.39266161
##  [551,] 1.30638771 0.78435682
##  [552,] 1.53096596 0.38065218
##  [553,] 0.65331450 0.99385507
##  [554,] 1.75276159 1.31681781
##  [555,] 0.25390550 0.98766575
##  [556,] 0.48586543 1.35153464
##  [557,] 1.08160229 1.30865338
##  [558,] 1.28560983 0.72668801
##  [559,] 0.38255865 1.61644644
##  [560,] 0.62248114 0.72987688
##  [561,] 1.18565836 1.06854593
##  [562,] 0.63824334 1.71211965
##  [563,] 1.21002241 1.56315871
##  [564,] 0.61941196 0.31559441
##  [565,] 1.67341991 1.01839761
##  [566,] 1.12321911 1.47345408
##  [567,] 0.40089470 1.23441487
##  [568,] 0.63769485 2.09147858
##  [569,] 1.24486533 1.12220566
##  [570,] 0.78564579 1.08061702
##  [571,] 2.22935584 0.28598602
##  [572,] 1.90208629 2.16702425
##  [573,] 1.66935618 1.71889417
##  [574,] 1.19900764 1.19051768
##  [575,] 0.62766805 0.78524603
##  [576,] 1.28445853 0.78980169
##  [577,] 1.86484644 2.34963179
##  [578,] 1.03829249 1.59728215
##  [579,] 0.63671384 0.75044895
##  [580,] 2.50292491 0.93780208
##  [581,] 0.37516371 0.59890162
##  [582,] 0.48996392 1.52667970
##  [583,] 0.84307879 1.18399962
##  [584,] 0.75631322 0.80190642
##  [585,] 1.16827988 0.73508122
##  [586,] 2.27347642 1.07754478
##  [587,] 0.89686664 1.83090309
##  [588,] 0.74283516 0.96726170
##  [589,] 1.16454632 1.33286414
##  [590,] 1.24404793 1.44060689
##  [591,] 1.60977098 1.78888108
##  [592,] 0.99131616 0.63625552
##  [593,] 0.98073238 0.66520460
##  [594,] 1.01679951 1.43951787
##  [595,] 1.00433221 1.19171935
##  [596,] 0.49212137 0.59876398
##  [597,] 0.72732049 0.30557612
##  [598,] 0.71362952 0.69008147
##  [599,] 1.11159525 0.34646844
##  [600,] 2.00884113 1.65720907
##  [601,] 0.62772671 0.91317700
##  [602,] 1.30121045 1.69172382
##  [603,] 0.71036415 1.26388622
##  [604,] 1.27706045 0.45624611
##  [605,] 2.22044770 0.59001624
##  [606,] 1.51558860 1.06470252
##  [607,] 0.47803283 0.40454637
##  [608,] 2.19996700 2.05975168
##  [609,] 1.80480165 0.05907415
##  [610,] 2.08356367 1.20237091
##  [611,] 1.59582933 1.68143149
##  [612,] 0.47271249 0.95826757
##  [613,] 0.40382286 0.55853308
##  [614,] 1.01307912 2.18091574
##  [615,] 0.60233015 0.79918225
##  [616,] 1.09009265 0.54330031
##  [617,] 1.81616384 1.51231752
##  [618,] 1.47209049 1.28719389
##  [619,] 0.59686268 0.24376475
##  [620,] 0.71795933 0.71062170
##  [621,] 0.45170542 1.00393586
##  [622,] 0.51719015 1.82516019
##  [623,] 1.26477670 0.80437741
##  [624,] 1.59283420 1.41972140
##  [625,] 0.25930215 0.55027087
##  [626,] 1.23112616 0.76449845
##  [627,] 0.19529858 0.33440116
##  [628,] 0.91177346 2.28568696
##  [629,] 0.68974129 0.71167988
##  [630,] 0.78595614 0.42168081
##  [631,] 0.76992509 1.26912726
##  [632,] 1.29915270 0.56109083
##  [633,] 0.20474651 1.03397507
##  [634,] 1.27267828 0.80808972
##  [635,] 1.81401352 1.09141129
##  [636,] 0.82354846 1.03765844
##  [637,] 0.93069278 1.17030052
##  [638,] 0.54519290 0.36851374
##  [639,] 0.64603993 0.77543366
##  [640,] 0.88082500 0.20232891
##  [641,] 0.79855622 1.01308145
##  [642,] 1.14376266 2.00900053
##  [643,] 0.47705972 0.48297703
##  [644,] 1.45388143 1.03062923
##  [645,] 0.79224936 1.22357240
##  [646,] 0.62502358 1.00599605
##  [647,] 2.13153701 1.60434553
##  [648,] 1.66196902 0.58885346
##  [649,] 0.74242778 1.68312606
##  [650,] 0.90770363 0.70970127
##  [651,] 2.05902837 1.00935014
##  [652,] 1.31594481 0.44081037
##  [653,] 1.51873454 0.40077612
##  [654,] 0.56920896 1.01841410
##  [655,] 0.96682206 0.90113297
##  [656,] 0.61813417 0.68985043
##  [657,] 1.43788196 2.30596089
##  [658,] 1.38351428 0.80354737
##  [659,] 0.97140525 0.89179752
##  [660,] 1.09958776 0.47628500
##  [661,] 1.85878410 0.68967588
##  [662,] 1.78831839 0.21004039
##  [663,] 0.19578523 0.67381809
##  [664,] 0.73318758 0.40480387
##  [665,] 2.59211359 0.24161558
##  [666,] 0.59893711 1.62265649
##  [667,] 0.85179708 0.92419481
##  [668,] 0.24411332 0.30596091
##  [669,] 1.31311636 1.52722177
##  [670,] 1.10997389 0.23736345
##  [671,] 1.06878124 0.97423772
##  [672,] 1.09009709 1.67234844
##  [673,] 0.49896324 0.64922086
##  [674,] 0.81276096 1.89428970
##  [675,] 1.26611323 0.42244865
##  [676,] 0.98759782 1.16934923
##  [677,] 0.84542633 0.55506035
##  [678,] 1.59116691 0.77960486
##  [679,] 0.74183314 0.66936736
##  [680,] 0.73715435 1.17574445
##  [681,] 0.39988470 0.71445234
##  [682,] 0.40765885 0.51412389
##  [683,] 0.68484366 1.19166494
##  [684,] 0.40929450 0.47773048
##  [685,] 1.54431991 1.12294344
##  [686,] 1.29634746 0.63095202
##  [687,] 0.65242003 0.64499235
##  [688,] 0.84655772 0.71860441
##  [689,] 0.65887210 1.28227541
##  [690,] 0.59879224 0.86005096
##  [691,] 0.51365916 1.37011153
##  [692,] 0.31277240 1.40899868
##  [693,] 1.33742112 0.46207699
##  [694,] 1.79956224 1.09422541
##  [695,] 0.90063815 1.14054657
##  [696,] 1.92610451 2.76604802
##  [697,] 1.09862636 1.15894212
##  [698,] 0.80525351 1.96218430
##  [699,] 0.69888540 0.73513398
##  [700,] 0.74703455 0.34379657
##  [701,] 0.82878274 0.50914062
##  [702,] 1.34772119 0.41788635
##  [703,] 2.05977059 0.37884983
##  [704,] 0.93779800 1.48301280
##  [705,] 1.74010724 0.68327549
##  [706,] 1.32456916 0.47980830
##  [707,] 0.42162042 1.32521920
##  [708,] 0.79797258 0.38981945
##  [709,] 0.67769598 1.31225224
##  [710,] 0.28193480 1.45015399
##  [711,] 0.82540986 1.10354493
##  [712,] 0.52794307 1.18293619
##  [713,] 1.15669221 0.92652165
##  [714,] 0.91824252 2.46954645
##  [715,] 1.50684802 0.55075907
##  [716,] 0.29308527 0.72095605
##  [717,] 1.04268318 1.07231305
##  [718,] 0.58359615 1.21954555
##  [719,] 1.47098059 0.49855718
##  [720,] 1.48084715 0.97248057
##  [721,] 0.42059568 0.48714215
##  [722,] 1.30976634 1.19626947
##  [723,] 1.06218257 1.46461071
##  [724,] 0.50323296 1.70440336
##  [725,] 2.00346118 0.96413146
##  [726,] 0.53304691 1.94526192
##  [727,] 0.93573104 1.54852620
##  [728,] 0.44424093 0.53788224
##  [729,] 0.62376174 0.76646992
##  [730,] 1.40859578 0.65276482
##  [731,] 1.40249692 0.52285462
##  [732,] 1.15721254 1.89114430
##  [733,] 1.03608608 0.45284090
##  [734,] 0.58621713 1.00552310
##  [735,] 1.56992725 1.19843172
##  [736,] 0.87014018 0.60363841
##  [737,] 0.95938960 0.78043886
##  [738,] 0.86674651 0.63415102
##  [739,] 2.10010337 0.34946959
##  [740,] 0.89058571 0.37816180
##  [741,] 0.87345482 0.91400375
##  [742,] 0.33455789 0.59056832
##  [743,] 1.09608866 0.55840844
##  [744,] 0.44544002 1.20725731
##  [745,] 1.28826760 1.61228873
##  [746,] 1.01345279 1.37232449
##  [747,] 1.26350376 0.67658429
##  [748,] 1.25190340 1.44747547
##  [749,] 1.27863555 0.58983274
##  [750,] 0.71892450 0.36714381
##  [751,] 0.71684762 1.53571811
##  [752,] 0.68603043 1.03450557
##  [753,] 0.41205998 1.93692000
##  [754,] 0.50608051 1.17464774
##  [755,] 0.27533791 0.78894147
##  [756,] 1.12427367 2.13090474
##  [757,] 1.51139079 1.59280830
##  [758,] 0.33947811 0.71355231
##  [759,] 0.71507160 2.59816209
##  [760,] 2.09234608 0.69258651
##  [761,] 2.59373532 1.24923545
##  [762,] 0.97872560 0.80804254
##  [763,] 1.01046653 0.63431090
##  [764,] 0.89466095 1.26004414
##  [765,] 1.52505664 0.93192125
##  [766,] 0.65600678 1.55014148
##  [767,] 0.61492283 0.77335023
##  [768,] 0.75713668 0.34716449
##  [769,] 1.00082548 1.31670123
##  [770,] 1.40659152 1.83096463
##  [771,] 0.23604628 1.57065173
##  [772,] 0.39847235 0.47639535
##  [773,] 0.52017362 1.18383054
##  [774,] 0.49184172 1.07053130
##  [775,] 2.56787104 0.74042433
##  [776,] 0.43728386 1.03076455
##  [777,] 0.85319250 0.55859613
##  [778,] 0.49222476 1.31369747
##  [779,] 0.32164914 0.15110377
##  [780,] 1.28600258 0.79744085
##  [781,] 0.88979710 0.42592599
##  [782,] 1.68947028 3.03491492
##  [783,] 1.18989534 0.30871490
##  [784,] 1.92428386 0.38726332
##  [785,] 0.38437407 1.20220129
##  [786,] 0.54221040 1.37099317
##  [787,] 0.38168445 0.76633890
##  [788,] 1.03915104 1.02235257
##  [789,] 1.03084637 1.64684671
##  [790,] 0.43061080 0.56642102
##  [791,] 0.51328456 1.34688260
##  [792,] 1.29644970 2.85866263
##  [793,] 0.29692717 1.34853639
##  [794,] 0.47025639 0.29698707
##  [795,] 1.43226220 0.83750070
##  [796,] 0.89525978 0.94574904
##  [797,] 1.01548792 1.34242671
##  [798,] 0.53487491 0.81501099
##  [799,] 1.60291748 2.87071701
##  [800,] 2.40589062 0.92662275
##  [801,] 0.89229824 1.19750688
##  [802,] 0.52245817 1.23072522
##  [803,] 0.66333178 1.62519208
##  [804,] 0.86317843 1.05664454
##  [805,] 0.96383731 0.96521547
##  [806,] 1.03285350 0.79248390
##  [807,] 1.46118471 0.78940838
##  [808,] 1.08545295 0.86022175
##  [809,] 0.43867199 1.04348941
##  [810,] 0.34237897 0.26747360
##  [811,] 1.06087144 2.36608147
##  [812,] 1.22402335 0.36713060
##  [813,] 1.09436705 0.34783260
##  [814,] 0.87654088 0.69008613
##  [815,] 0.23048964 1.64716060
##  [816,] 1.32844259 0.39071572
##  [817,] 1.59733762 2.42567485
##  [818,] 2.31894123 2.20890400
##  [819,] 1.79336059 0.93774208
##  [820,] 0.38595697 0.31228763
##  [821,] 0.54853247 0.73380999
##  [822,] 1.95429373 1.61757122
##  [823,] 0.84709130 0.36778146
##  [824,] 1.34862736 1.38422571
##  [825,] 1.28158941 1.48996730
##  [826,] 1.42333993 1.64183321
##  [827,] 0.53614003 1.17500527
##  [828,] 1.89775023 0.73837374
##  [829,] 1.01919020 0.69129810
##  [830,] 0.52752389 2.44558029
##  [831,] 0.92894097 0.66764360
##  [832,] 1.15387385 1.08354661
##  [833,] 1.62051849 2.27909122
##  [834,] 1.10286058 0.51916630
##  [835,] 0.37372946 0.44865874
##  [836,] 0.80461010 0.85691576
##  [837,] 0.92769680 0.77493997
##  [838,] 1.25660622 0.64623661
##  [839,] 1.11020383 1.20550121
##  [840,] 0.41985457 0.48252745
##  [841,] 0.58316789 0.37142260
##  [842,] 0.55927565 3.01739196
##  [843,] 2.49294857 0.53028335
##  [844,] 0.59207743 1.13499678
##  [845,] 0.81168199 0.50509652
##  [846,] 0.63828587 2.49604946
##  [847,] 0.11687059 0.56405744
##  [848,] 0.92071700 1.25707882
##  [849,] 0.62811910 0.54452991
##  [850,] 0.68864058 2.34108360
##  [851,] 1.98813449 2.15758482
##  [852,] 0.96636553 1.15987528
##  [853,] 0.50012678 0.61436256
##  [854,] 0.58883262 0.84292190
##  [855,] 1.07669410 0.53429298
##  [856,] 1.35131311 0.47019266
##  [857,] 1.99604596 0.70804712
##  [858,] 0.50319118 0.80845133
##  [859,] 1.28516871 1.18583863
##  [860,] 0.70068280 1.25085833
##  [861,] 1.14200310 1.41738918
##  [862,] 0.66431516 0.73082911
##  [863,] 0.60222834 1.30557232
##  [864,] 2.15892463 0.71163008
##  [865,] 0.29001653 0.21353531
##  [866,] 0.67092943 0.73784575
##  [867,] 0.54472953 1.20616442
##  [868,] 0.92688556 1.03085096
##  [869,] 0.65136718 0.82225178
##  [870,] 0.48609269 0.62278110
##  [871,] 1.20615815 0.49707669
##  [872,] 1.23351914 1.07788033
##  [873,] 1.64521706 1.99862264
##  [874,] 0.57741470 0.52914935
##  [875,] 0.34012342 0.47300133
##  [876,] 0.45124916 0.40009707
##  [877,] 0.70131351 0.50857264
##  [878,] 1.37888497 1.12916714
##  [879,] 2.50002322 0.69370740
##  [880,] 1.02864162 1.15426854
##  [881,] 1.29019573 1.00376523
##  [882,] 1.05102140 1.31999938
##  [883,] 0.74388326 0.76755485
##  [884,] 1.25291982 0.48428857
##  [885,] 1.54070068 0.97412670
##  [886,] 0.73817695 0.71396321
##  [887,] 1.03070231 0.80910627
##  [888,] 0.70733045 0.70076468
##  [889,] 1.49814598 0.53227756
##  [890,] 0.53024812 0.92260908
##  [891,] 1.08486925 1.93509196
##  [892,] 0.59940635 0.72311177
##  [893,] 0.57450092 1.35341804
##  [894,] 1.79405073 1.07477944
##  [895,] 0.21499918 0.35022510
##  [896,] 0.56998174 0.72052021
##  [897,] 1.02402833 2.41580674
##  [898,] 1.52631826 1.50248164
##  [899,] 1.50824926 1.07372116
##  [900,] 0.49334491 1.06871818
##  [901,] 1.41522717 2.84522148
##  [902,] 0.82790067 2.36896053
##  [903,] 1.60375665 0.79416784
##  [904,] 0.31340902 2.78047995
##  [905,] 0.85047621 1.11246046
##  [906,] 0.87359907 1.19679814
##  [907,] 0.35962695 0.72873514
##  [908,] 2.27598336 1.58114496
##  [909,] 0.64843460 0.31206881
##  [910,] 0.42488924 0.65103151
##  [911,] 0.62593719 0.91524567
##  [912,] 2.22092014 0.74220303
##  [913,] 0.98008490 0.92449568
##  [914,] 0.63629436 0.90078628
##  [915,] 0.71336855 0.63313088
##  [916,] 0.83124065 1.04338417
##  [917,] 1.55360325 0.69699097
##  [918,] 0.88758911 1.48967691
##  [919,] 0.81449812 0.72860423
##  [920,] 0.39791171 0.88909461
##  [921,] 0.84335710 0.56526315
##  [922,] 1.70928303 1.28024332
##  [923,] 0.61505221 1.02085007
##  [924,] 1.57561851 1.09573411
##  [925,] 0.94258983 1.05489614
##  [926,] 0.63001795 0.91220001
##  [927,] 1.15983174 1.00649251
##  [928,] 0.79192310 1.32475186
##  [929,] 0.32612830 0.53902589
##  [930,] 0.06507824 0.46683767
##  [931,] 0.99942698 1.01099141
##  [932,] 0.85819718 1.35158553
##  [933,] 2.27027143 0.70256989
##  [934,] 0.85748615 1.08375184
##  [935,] 0.50984221 0.33737360
##  [936,] 1.88506174 0.20621667
##  [937,] 0.97008207 1.01653288
##  [938,] 0.66035613 0.70767619
##  [939,] 0.60292447 0.56466460
##  [940,] 1.65659554 1.75001230
##  [941,] 1.14036195 1.38914840
##  [942,] 0.68841710 1.46491361
##  [943,] 1.99922885 0.41672160
##  [944,] 0.38753706 1.13124173
##  [945,] 1.24490350 1.28126704
##  [946,] 0.72778573 1.44571945
##  [947,] 0.54906314 0.58453882
##  [948,] 1.81598942 0.43549249
##  [949,] 0.34033894 0.79579911
##  [950,] 0.80848042 0.89848907
##  [951,] 1.03917435 0.62845041
##  [952,] 1.34181578 1.46777137
##  [953,] 0.69368863 0.81119101
##  [954,] 1.17242356 0.52461616
##  [955,] 1.86842717 1.59466127
##  [956,] 0.66835349 1.04369243
##  [957,] 0.44460128 1.48373771
##  [958,] 1.27759082 0.72572174
##  [959,] 1.32060817 0.60752432
##  [960,] 0.80202177 0.90071903
##  [961,] 0.60817740 0.45518140
##  [962,] 1.61741602 0.16625947
##  [963,] 0.17790034 1.63388810
##  [964,] 1.10735725 1.47725023
##  [965,] 1.10967454 2.39772392
##  [966,] 0.51993983 1.28254404
##  [967,] 0.36946811 1.26305619
##  [968,] 0.71352134 1.37349992
##  [969,] 1.04524829 1.38124881
##  [970,] 0.40747450 0.40406030
##  [971,] 0.81115614 1.53944929
##  [972,] 1.10944411 0.85249147
##  [973,] 1.01315505 0.46267533
##  [974,] 1.97265014 0.71529619
##  [975,] 0.69378163 1.35512595
##  [976,] 1.26242201 0.38993369
##  [977,] 0.40984163 0.79500240
##  [978,] 1.01136820 0.90020576
##  [979,] 1.25290904 0.27863702
##  [980,] 1.89168281 1.07017774
##  [981,] 0.52213994 1.78279136
##  [982,] 0.50858630 1.15337107
##  [983,] 0.52640670 0.86512148
##  [984,] 0.57360037 1.16488051
##  [985,] 1.29013824 0.70920965
##  [986,] 0.73614325 1.25243904
##  [987,] 0.98448407 0.42224632
##  [988,] 1.47705088 0.70825188
##  [989,] 0.22408898 0.28103410
##  [990,] 2.19876000 0.39834852
##  [991,] 1.78030767 1.17992941
##  [992,] 1.34823547 1.47540577
##  [993,] 0.86095407 1.18363061
##  [994,] 0.84773532 0.16795104
##  [995,] 0.60582437 1.31650125
##  [996,] 0.75056417 1.51325566
##  [997,] 0.85279635 0.33303172
##  [998,] 1.00470509 1.63167659
##  [999,] 1.02546663 0.91057727
## 
## $model.matrix
##    (Intercept) microsite1 shrub_density
## 1            1          1            11
## 2            1          1            12
## 3            1         -1             0
## 4            1         -1             0
## 5            1          1            11
## 6            1          1            10
## 7            1         -1             0
## 8            1         -1             0
## 9            1          1            14
## 10           1          1            13
## 11           1         -1             0
## 12           1         -1             0
## 13           1          1            11
## 14           1          1            11
## 15           1         -1             0
## 16           1          1            10
## 17           1          1            11
## 18           1          1            11
## 19           1          1            10
## 20           1         -1             0
## 21           1         -1             0
## 22           1         -1             0
## 23           1         -1             0
## 
## $terms
## pca_data_2023 ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_2023, microsite, shrub_density)
## attr(,"factors")
##               microsite shrub_density microsite:shrub_density
## pca_data_2023         0             0                       0
## microsite             1             0                       1
## shrub_density         0             1                       1
## attr(,"term.labels")
## [1] "microsite"               "shrub_density"          
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## 
## attr(,"class")
## [1] "adonis"
dist_2023 <- vegdist(pca_data_2023, species = "bray")
res_2023 <- pcoa(dist_2023)
p2 <- as.data.frame(res_2023$vectors)%>%
  dplyr::select(Axis.1, Axis.2) %>%
  bind_cols(env_2023,.)

ggplot(p2, aes(Axis.1, Axis.2, group = microsite)) +
  geom_point(aes(color = microsite)) +
  geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
  scale_color_brewer(palette = "Set1") +
  labs(color = "", subtitle = "labels denote plot identity")

model02 <- betadisper(dist_2023, env_2023$microsite)
model02
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist_2023, group = env_2023$microsite)
## 
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
## 
## Average distance to median:
## Density    Open 
##  0.4638  0.5348 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model02)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq F value Pr(>F)
## Groups     1 0.02894 0.028937  1.5275 0.2301
## Residuals 21 0.39782 0.018944
permutest(model02,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
## Groups     1 0.02894 0.028937 1.5275     99   0.27
## Residuals 21 0.39782 0.018944                     
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##         Density Open
## Density         0.25
## Open    0.23013
model02.HSD <- TukeyHSD(model02)
boxplot(model02)

model03 <- betadisper(dist_2023, env_2023$shrub_density)
model03
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist_2023, group = env_2023$shrub_density)
## 
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
## 
## Average distance to median:
##      0     10     11     12     13     14 
## 0.5348 0.3051 0.4393 0.0000 0.0000 0.0000 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model03)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq F value   Pr(>F)   
## Groups     5 0.71375 0.142751    4.86 0.006083 **
## Residuals 17 0.49934 0.029373                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model03,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq    F N.Perm Pr(>F)  
## Groups     5 0.71375 0.142751 4.86     99   0.02 *
## Residuals 17 0.49934 0.029373                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##           0       10       11 12 13 14
## 0           0.080000 0.230000         
## 10 0.059818          0.460000         
## 11 0.192393 0.435177                  
## 12                                    
## 13                                    
## 14
model03.HSD <- TukeyHSD(model03)
boxplot(model03)

model04 <- betadisper(dist_2023, env_2023$site)
model04
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist_2023, group = env_2023$site)
## 
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
## 
## Average distance to median:
## Carrizo  Cuyama  Tecopa 
##  0.5093  0.4594  0.4488 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model04)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq   Mean Sq F value Pr(>F)
## Groups     2 0.01656 0.0082795  0.4577 0.6392
## Residuals 20 0.36181 0.0180906
permutest(model04,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
## Groups     2 0.01656 0.0082795 0.4577     99   0.66
## Residuals 20 0.36181 0.0180906                     
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##         Carrizo  Cuyama Tecopa
## Carrizo         0.60000   0.14
## Cuyama  0.56406           0.90
## Tecopa  0.14036 0.89377
model04.HSD <- TukeyHSD(model04)
boxplot(model04)

### From running the community compositions both in 2022 and 2023 it seems the community compositions across densities, microsites, and sites are all somewhat similar with no significant differences.

Run PCOAs for both years combined!

photo_final <- read.csv("observations_final.csv")
summary(photo_final)
##     region              site            site_code          microsite        
##  Length:250716      Length:250716      Length:250716      Length:250716     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       plot           cam_ID         month               date          
##  Min.   :1.000   Min.   :1.000   Length:250716      Length:250716     
##  1st Qu.:1.000   1st Qu.:1.000   Class :character   Class :character  
##  Median :2.000   Median :1.000   Mode  :character   Mode  :character  
##  Mean   :1.772   Mean   :1.498                                        
##  3rd Qu.:2.000   3rd Qu.:2.000                                        
##  Max.   :4.000   Max.   :2.000                                        
##       year      shrub_density         rep         identified_by     
##  Min.   :2022   Min.   : 0.000   Min.   :     1   Length:250716     
##  1st Qu.:2023   1st Qu.: 0.000   1st Qu.: 21744   Class :character  
##  Median :2023   Median :10.000   Median : 67344   Mode  :character  
##  Mean   :2023   Mean   : 5.878   Mean   : 77566                     
##  3rd Qu.:2023   3rd Qu.:11.000   3rd Qu.:130022                     
##  Max.   :2023   Max.   :14.000   Max.   :192701                     
##    filename          timestamp           animal.hit         class          
##  Length:250716      Length:250716      Min.   :0.00000   Length:250716     
##  Class :character   Class :character   1st Qu.:0.00000   Class :character  
##  Mode  :character   Mode  :character   Median :0.00000   Mode  :character  
##                                        Mean   :0.01923                     
##                                        3rd Qu.:0.00000                     
##                                        Max.   :1.00000                     
##     order              family             genus             species         
##  Length:250716      Length:250716      Length:250716      Length:250716     
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##  common_name        number_of_objects
##  Length:250716      Min.   : 1       
##  Class :character   1st Qu.: 1       
##  Mode  :character   Median : 1       
##                     Mean   : 1       
##                     3rd Qu.: 1       
##                     Max.   :12
photo_final <- photo_final %>%
  filter(common_name != "Human")
photo_final <- photo_final %>%
  filter(common_name != "Human-Camera Trapper")
photo_final <- photo_final %>%
  filter(common_name != "Domestic Dog")
photo_final <- photo_final %>%
  filter(common_name != "Vehicle")
photo_final <- photo_final %>%
  dplyr::filter(common_name != "Insect")
photo_final <- photo_final %>%
  dplyr::filter(common_name != "Animal")
photo_final <- photo_final %>%
  dplyr::filter(common_name != "Bird")
  
count.hit_final <- photo_final %>%
  count(animal.hit) %>%
  na.omit()
summary(count.hit_final)
##    animal.hit         n         
##  Min.   :0.00   Min.   :  3717  
##  1st Qu.:0.25   1st Qu.: 64261  
##  Median :0.50   Median :124806  
##  Mean   :0.50   Mean   :124806  
##  3rd Qu.:0.75   3rd Qu.:185350  
##  Max.   :1.00   Max.   :245894
# Wow we have a 1.489% capture rate for the project!
### Animal Observations by Site_Code
animals_by_sitecode_final <- photo_final%>%
  group_by(site_code, microsite, common_name) %>%
  summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode_final <- animals_by_sitecode_final %>%
  filter(common_name != "Blank") %>% filter(common_name != "No CV Result") 
animals_by_site_final <- photo_final %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site_final <- animals_by_site_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
animals_by_density_final<- photo_final %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density_final <- animals_by_density_final %>% filter(common_name != "Blank")  %>% filter(common_name != "No CV Result")
Total_Observations_final <- photo_final %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank")  %>% filter(common_name != "No CV Result") %>% filter(common_name != "Mammal")
density_obvs_final <- merge(animals_by_density_final, Total_Observations_final, all = TRUE) %>% filter(common_name != "Mammal")
density_obvs_final$percent_presence <- density_obvs_final$captures/density_obvs_final$total
m3<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_final)
anova(m3, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: total
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     55      27821              
## microsite              1     29.6        54      27791 5.449e-08 ***
## common_name           32  27791.1        22          0 < 2.2e-16 ***
## microsite:common_name 22      0.0         0          0         1    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e3 <- emmeans(m3, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
e3
## $emmeans
##  common_name                    emmean      SE  df asymp.LCL asymp.UCL
##  American Robin                 nonEst      NA  NA        NA        NA
##  Black-tailed Jackrabbit         5.878 0.03742 Inf    5.8044     5.951
##  Black-throated Sparrow         nonEst      NA  NA        NA        NA
##  Blunt-nosed Leopard Lizard      1.386 0.35355 Inf    0.6933     2.079
##  Bobcat                          1.792 0.28868 Inf    1.2260     2.358
##  Brewer's Blackbird              2.833 0.17150 Inf    2.4971     3.169
##  California Ground Squirrel      5.447 0.04642 Inf    5.3557     5.538
##  California Pocket Mouse         1.386 0.35355 Inf    0.6933     2.079
##  California Quail                3.258 0.13867 Inf    2.9863     3.530
##  California Thrasher            nonEst      NA  NA        NA        NA
##  Common Raven                    4.394 0.07857 Inf    4.2405     4.548
##  Coyote                          4.898 0.06108 Inf    4.7781     5.018
##  Desert Cottontail               4.143 0.08909 Inf    3.9685     4.318
##  Desert Iguana                  nonEst      NA  NA        NA        NA
##  Giant Kangaroo Rat              4.220 0.08575 Inf    4.0514     4.388
##  Great White Egret              nonEst      NA  NA        NA        NA
##  Greater Roadrunner              1.946 0.26726 Inf    1.4221     2.470
##  Heermann's Kangaroo Rat         7.782 0.01444 Inf    7.7537     7.810
##  Horned Lark                    nonEst      NA  NA        NA        NA
##  Killdeer                       nonEst      NA  NA        NA        NA
##  Kit Fox                         2.708 0.18257 Inf    2.3502     3.066
##  Lark Sparrow                    1.946 0.26726 Inf    1.4221     2.470
##  Loggerhead Shrike               2.565 0.19612 Inf    2.1806     2.949
##  Merriam's Kangaroo Rat          2.565 0.19612 Inf    2.1806     2.949
##  Mohave Ground Squirrel         nonEst      NA  NA        NA        NA
##  Mourning Dove                   1.609 0.31623 Inf    0.9896     2.229
##  Nelson's Antelope Squirrel      5.142 0.05407 Inf    5.0357     5.248
##  Red-tailed Hawk                nonEst      NA  NA        NA        NA
##  Salinas Pocket Mouse            1.386 0.35355 Inf    0.6933     2.079
##  Say's Phoebe                    1.386 0.35355 Inf    0.6933     2.079
##  Vesper Sparrow                  2.565 0.19612 Inf    2.1806     2.949
##  Western whiptail               nonEst      NA  NA        NA        NA
##  White-tailed Antelope Squirrel  1.792 0.28868 Inf    1.2260     2.358
## 
## Results are averaged over the levels of: microsite 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                                        estimate
##  American Robin - (Black-tailed Jackrabbit)                        nonEst
##  American Robin - (Black-throated Sparrow)                         nonEst
##  American Robin - (Blunt-nosed Leopard Lizard)                     nonEst
##  American Robin - Bobcat                                           nonEst
##  American Robin - Brewer's Blackbird                               nonEst
##  American Robin - California Ground Squirrel                       nonEst
##  American Robin - California Pocket Mouse                          nonEst
##  American Robin - California Quail                                 nonEst
##  American Robin - California Thrasher                              nonEst
##  American Robin - Common Raven                                     nonEst
##  American Robin - Coyote                                           nonEst
##  American Robin - Desert Cottontail                                nonEst
##  American Robin - Desert Iguana                                    nonEst
##  American Robin - Giant Kangaroo Rat                               nonEst
##  American Robin - Great White Egret                                nonEst
##  American Robin - Greater Roadrunner                               nonEst
##  American Robin - Heermann's Kangaroo Rat                          nonEst
##  American Robin - Horned Lark                                      nonEst
##  American Robin - Killdeer                                         nonEst
##  American Robin - Kit Fox                                          nonEst
##  American Robin - Lark Sparrow                                     nonEst
##  American Robin - Loggerhead Shrike                                nonEst
##  American Robin - Merriam's Kangaroo Rat                           nonEst
##  American Robin - Mohave Ground Squirrel                           nonEst
##  American Robin - Mourning Dove                                    nonEst
##  American Robin - Nelson's Antelope Squirrel                       nonEst
##  American Robin - (Red-tailed Hawk)                                nonEst
##  American Robin - Salinas Pocket Mouse                             nonEst
##  American Robin - Say's Phoebe                                     nonEst
##  American Robin - Vesper Sparrow                                   nonEst
##  American Robin - Western whiptail                                 nonEst
##  American Robin - (White-tailed Antelope Squirrel)                 nonEst
##  (Black-tailed Jackrabbit) - (Black-throated Sparrow)              nonEst
##  (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard)          4.4914
##  (Black-tailed Jackrabbit) - Bobcat                                4.0860
##  (Black-tailed Jackrabbit) - Brewer's Blackbird                    3.0445
##  (Black-tailed Jackrabbit) - California Ground Squirrel            0.4310
##  (Black-tailed Jackrabbit) - California Pocket Mouse               4.4914
##  (Black-tailed Jackrabbit) - California Quail                      2.6196
##  (Black-tailed Jackrabbit) - California Thrasher                   nonEst
##  (Black-tailed Jackrabbit) - Common Raven                          1.4833
##  (Black-tailed Jackrabbit) - Coyote                                0.9799
##  (Black-tailed Jackrabbit) - Desert Cottontail                     1.7346
##  (Black-tailed Jackrabbit) - Desert Iguana                         nonEst
##  (Black-tailed Jackrabbit) - Giant Kangaroo Rat                    1.6582
##  (Black-tailed Jackrabbit) - Great White Egret                     nonEst
##  (Black-tailed Jackrabbit) - Greater Roadrunner                    3.9318
##  (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat              -1.9042
##  (Black-tailed Jackrabbit) - Horned Lark                           nonEst
##  (Black-tailed Jackrabbit) - Killdeer                              nonEst
##  (Black-tailed Jackrabbit) - Kit Fox                               3.1697
##  (Black-tailed Jackrabbit) - Lark Sparrow                          3.9318
##  (Black-tailed Jackrabbit) - Loggerhead Shrike                     3.3128
##  (Black-tailed Jackrabbit) - Merriam's Kangaroo Rat                3.3128
##  (Black-tailed Jackrabbit) - Mohave Ground Squirrel                nonEst
##  (Black-tailed Jackrabbit) - Mourning Dove                         4.2683
##  (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel            0.7361
##  (Black-tailed Jackrabbit) - (Red-tailed Hawk)                     nonEst
##  (Black-tailed Jackrabbit) - Salinas Pocket Mouse                  4.4914
##  (Black-tailed Jackrabbit) - Say's Phoebe                          4.4914
##  (Black-tailed Jackrabbit) - Vesper Sparrow                        3.3128
##  (Black-tailed Jackrabbit) - Western whiptail                      nonEst
##  (Black-tailed Jackrabbit) - (White-tailed Antelope Squirrel)      4.0860
##  (Black-throated Sparrow) - (Blunt-nosed Leopard Lizard)           nonEst
##  (Black-throated Sparrow) - Bobcat                                 nonEst
##  (Black-throated Sparrow) - Brewer's Blackbird                     nonEst
##  (Black-throated Sparrow) - California Ground Squirrel             nonEst
##  (Black-throated Sparrow) - California Pocket Mouse                nonEst
##  (Black-throated Sparrow) - California Quail                       nonEst
##  (Black-throated Sparrow) - California Thrasher                    nonEst
##  (Black-throated Sparrow) - Common Raven                           nonEst
##  (Black-throated Sparrow) - Coyote                                 nonEst
##  (Black-throated Sparrow) - Desert Cottontail                      nonEst
##  (Black-throated Sparrow) - Desert Iguana                          nonEst
##  (Black-throated Sparrow) - Giant Kangaroo Rat                     nonEst
##  (Black-throated Sparrow) - Great White Egret                      nonEst
##  (Black-throated Sparrow) - Greater Roadrunner                     nonEst
##  (Black-throated Sparrow) - Heermann's Kangaroo Rat                nonEst
##  (Black-throated Sparrow) - Horned Lark                            nonEst
##  (Black-throated Sparrow) - Killdeer                               nonEst
##  (Black-throated Sparrow) - Kit Fox                                nonEst
##  (Black-throated Sparrow) - Lark Sparrow                           nonEst
##  (Black-throated Sparrow) - Loggerhead Shrike                      nonEst
##  (Black-throated Sparrow) - Merriam's Kangaroo Rat                 nonEst
##  (Black-throated Sparrow) - Mohave Ground Squirrel                 nonEst
##  (Black-throated Sparrow) - Mourning Dove                          nonEst
##  (Black-throated Sparrow) - Nelson's Antelope Squirrel             nonEst
##  (Black-throated Sparrow) - (Red-tailed Hawk)                      nonEst
##  (Black-throated Sparrow) - Salinas Pocket Mouse                   nonEst
##  (Black-throated Sparrow) - Say's Phoebe                           nonEst
##  (Black-throated Sparrow) - Vesper Sparrow                         nonEst
##  (Black-throated Sparrow) - Western whiptail                       nonEst
##  (Black-throated Sparrow) - (White-tailed Antelope Squirrel)       nonEst
##  (Blunt-nosed Leopard Lizard) - Bobcat                            -0.4055
##  (Blunt-nosed Leopard Lizard) - Brewer's Blackbird                -1.4469
##  (Blunt-nosed Leopard Lizard) - California Ground Squirrel        -4.0604
##  (Blunt-nosed Leopard Lizard) - California Pocket Mouse            0.0000
##  (Blunt-nosed Leopard Lizard) - California Quail                  -1.8718
##  (Blunt-nosed Leopard Lizard) - California Thrasher                nonEst
##  (Blunt-nosed Leopard Lizard) - Common Raven                      -3.0082
##  (Blunt-nosed Leopard Lizard) - Coyote                            -3.5115
##  (Blunt-nosed Leopard Lizard) - Desert Cottontail                 -2.7568
##  (Blunt-nosed Leopard Lizard) - Desert Iguana                      nonEst
##  (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat                -2.8332
##  (Blunt-nosed Leopard Lizard) - Great White Egret                  nonEst
##  (Blunt-nosed Leopard Lizard) - Greater Roadrunner                -0.5596
##  (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat           -6.3957
##  (Blunt-nosed Leopard Lizard) - Horned Lark                        nonEst
##  (Blunt-nosed Leopard Lizard) - Killdeer                           nonEst
##  (Blunt-nosed Leopard Lizard) - Kit Fox                           -1.3218
##  (Blunt-nosed Leopard Lizard) - Lark Sparrow                      -0.5596
##  (Blunt-nosed Leopard Lizard) - Loggerhead Shrike                 -1.1787
##  (Blunt-nosed Leopard Lizard) - Merriam's Kangaroo Rat            -1.1787
##  (Blunt-nosed Leopard Lizard) - Mohave Ground Squirrel             nonEst
##  (Blunt-nosed Leopard Lizard) - Mourning Dove                     -0.2231
##  (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel        -3.7554
##  (Blunt-nosed Leopard Lizard) - (Red-tailed Hawk)                  nonEst
##  (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse               0.0000
##  (Blunt-nosed Leopard Lizard) - Say's Phoebe                       0.0000
##  (Blunt-nosed Leopard Lizard) - Vesper Sparrow                    -1.1787
##  (Blunt-nosed Leopard Lizard) - Western whiptail                   nonEst
##  (Blunt-nosed Leopard Lizard) - (White-tailed Antelope Squirrel)  -0.4055
##  Bobcat - Brewer's Blackbird                                      -1.0415
##  Bobcat - California Ground Squirrel                              -3.6550
##  Bobcat - California Pocket Mouse                                  0.4055
##  Bobcat - California Quail                                        -1.4663
##  Bobcat - California Thrasher                                      nonEst
##  Bobcat - Common Raven                                            -2.6027
##  Bobcat - Coyote                                                  -3.1061
##  Bobcat - Desert Cottontail                                       -2.3514
##  Bobcat - Desert Iguana                                            nonEst
##  Bobcat - Giant Kangaroo Rat                                      -2.4277
##  Bobcat - Great White Egret                                        nonEst
##  Bobcat - Greater Roadrunner                                      -0.1542
##  Bobcat - Heermann's Kangaroo Rat                                 -5.9902
##  Bobcat - Horned Lark                                              nonEst
##  Bobcat - Killdeer                                                 nonEst
##  Bobcat - Kit Fox                                                 -0.9163
##  Bobcat - Lark Sparrow                                            -0.1542
##  Bobcat - Loggerhead Shrike                                       -0.7732
##  Bobcat - Merriam's Kangaroo Rat                                  -0.7732
##  Bobcat - Mohave Ground Squirrel                                   nonEst
##  Bobcat - Mourning Dove                                            0.1823
##  Bobcat - Nelson's Antelope Squirrel                              -3.3499
##  Bobcat - (Red-tailed Hawk)                                        nonEst
##  Bobcat - Salinas Pocket Mouse                                     0.4055
##  Bobcat - Say's Phoebe                                             0.4055
##  Bobcat - Vesper Sparrow                                          -0.7732
##  Bobcat - Western whiptail                                         nonEst
##  Bobcat - (White-tailed Antelope Squirrel)                         0.0000
##  Brewer's Blackbird - California Ground Squirrel                  -2.6135
##  Brewer's Blackbird - California Pocket Mouse                      1.4469
##  Brewer's Blackbird - California Quail                            -0.4249
##  Brewer's Blackbird - California Thrasher                          nonEst
##  Brewer's Blackbird - Common Raven                                -1.5612
##  Brewer's Blackbird - Coyote                                      -2.0646
##  Brewer's Blackbird - Desert Cottontail                           -1.3099
##  Brewer's Blackbird - Desert Iguana                                nonEst
##  Brewer's Blackbird - Giant Kangaroo Rat                          -1.3863
##  Brewer's Blackbird - Great White Egret                            nonEst
##  Brewer's Blackbird - Greater Roadrunner                           0.8873
##  Brewer's Blackbird - Heermann's Kangaroo Rat                     -4.9488
##  Brewer's Blackbird - Horned Lark                                  nonEst
##  Brewer's Blackbird - Killdeer                                     nonEst
##  Brewer's Blackbird - Kit Fox                                      0.1252
##  Brewer's Blackbird - Lark Sparrow                                 0.8873
##  Brewer's Blackbird - Loggerhead Shrike                            0.2683
##  Brewer's Blackbird - Merriam's Kangaroo Rat                       0.2683
##  Brewer's Blackbird - Mohave Ground Squirrel                       nonEst
##  Brewer's Blackbird - Mourning Dove                                1.2238
##  Brewer's Blackbird - Nelson's Antelope Squirrel                  -2.3085
##  Brewer's Blackbird - (Red-tailed Hawk)                            nonEst
##  Brewer's Blackbird - Salinas Pocket Mouse                         1.4469
##  Brewer's Blackbird - Say's Phoebe                                 1.4469
##  Brewer's Blackbird - Vesper Sparrow                               0.2683
##  Brewer's Blackbird - Western whiptail                             nonEst
##  Brewer's Blackbird - (White-tailed Antelope Squirrel)             1.0415
##  California Ground Squirrel - California Pocket Mouse              4.0604
##  California Ground Squirrel - California Quail                     2.1886
##  California Ground Squirrel - California Thrasher                  nonEst
##  California Ground Squirrel - Common Raven                         1.0523
##  California Ground Squirrel - Coyote                               0.5489
##  California Ground Squirrel - Desert Cottontail                    1.3036
##  California Ground Squirrel - Desert Iguana                        nonEst
##  California Ground Squirrel - Giant Kangaroo Rat                   1.2272
##  California Ground Squirrel - Great White Egret                    nonEst
##  California Ground Squirrel - Greater Roadrunner                   3.5008
##  California Ground Squirrel - Heermann's Kangaroo Rat             -2.3352
##  California Ground Squirrel - Horned Lark                          nonEst
##  California Ground Squirrel - Killdeer                             nonEst
##  California Ground Squirrel - Kit Fox                              2.7387
##  California Ground Squirrel - Lark Sparrow                         3.5008
##  California Ground Squirrel - Loggerhead Shrike                    2.8818
##  California Ground Squirrel - Merriam's Kangaroo Rat               2.8818
##  California Ground Squirrel - Mohave Ground Squirrel               nonEst
##  California Ground Squirrel - Mourning Dove                        3.8373
##  California Ground Squirrel - Nelson's Antelope Squirrel           0.3051
##  California Ground Squirrel - (Red-tailed Hawk)                    nonEst
##  California Ground Squirrel - Salinas Pocket Mouse                 4.0604
##  California Ground Squirrel - Say's Phoebe                         4.0604
##  California Ground Squirrel - Vesper Sparrow                       2.8818
##  California Ground Squirrel - Western whiptail                     nonEst
##  California Ground Squirrel - (White-tailed Antelope Squirrel)     3.6550
##  California Pocket Mouse - California Quail                       -1.8718
##  California Pocket Mouse - California Thrasher                     nonEst
##  California Pocket Mouse - Common Raven                           -3.0082
##  California Pocket Mouse - Coyote                                 -3.5115
##  California Pocket Mouse - Desert Cottontail                      -2.7568
##  California Pocket Mouse - Desert Iguana                           nonEst
##  California Pocket Mouse - Giant Kangaroo Rat                     -2.8332
##  California Pocket Mouse - Great White Egret                       nonEst
##  California Pocket Mouse - Greater Roadrunner                     -0.5596
##  California Pocket Mouse - Heermann's Kangaroo Rat                -6.3957
##  California Pocket Mouse - Horned Lark                             nonEst
##  California Pocket Mouse - Killdeer                                nonEst
##  California Pocket Mouse - Kit Fox                                -1.3218
##  California Pocket Mouse - Lark Sparrow                           -0.5596
##  California Pocket Mouse - Loggerhead Shrike                      -1.1787
##  California Pocket Mouse - Merriam's Kangaroo Rat                 -1.1787
##  California Pocket Mouse - Mohave Ground Squirrel                  nonEst
##  California Pocket Mouse - Mourning Dove                          -0.2231
##  California Pocket Mouse - Nelson's Antelope Squirrel             -3.7554
##  California Pocket Mouse - (Red-tailed Hawk)                       nonEst
##  California Pocket Mouse - Salinas Pocket Mouse                    0.0000
##  California Pocket Mouse - Say's Phoebe                            0.0000
##  California Pocket Mouse - Vesper Sparrow                         -1.1787
##  California Pocket Mouse - Western whiptail                        nonEst
##  California Pocket Mouse - (White-tailed Antelope Squirrel)       -0.4055
##  California Quail - California Thrasher                            nonEst
##  California Quail - Common Raven                                  -1.1364
##  California Quail - Coyote                                        -1.6397
##  California Quail - Desert Cottontail                             -0.8850
##  California Quail - Desert Iguana                                  nonEst
##  California Quail - Giant Kangaroo Rat                            -0.9614
##  California Quail - Great White Egret                              nonEst
##  California Quail - Greater Roadrunner                             1.3122
##  California Quail - Heermann's Kangaroo Rat                       -4.5239
##  California Quail - Horned Lark                                    nonEst
##  California Quail - Killdeer                                       nonEst
##  California Quail - Kit Fox                                        0.5500
##  California Quail - Lark Sparrow                                   1.3122
##  California Quail - Loggerhead Shrike                              0.6931
##  California Quail - Merriam's Kangaroo Rat                         0.6931
##  California Quail - Mohave Ground Squirrel                         nonEst
##  California Quail - Mourning Dove                                  1.6487
##  California Quail - Nelson's Antelope Squirrel                    -1.8836
##  California Quail - (Red-tailed Hawk)                              nonEst
##  California Quail - Salinas Pocket Mouse                           1.8718
##  California Quail - Say's Phoebe                                   1.8718
##  California Quail - Vesper Sparrow                                 0.6931
##  California Quail - Western whiptail                               nonEst
##  California Quail - (White-tailed Antelope Squirrel)               1.4663
##  California Thrasher - Common Raven                                nonEst
##  California Thrasher - Coyote                                      nonEst
##  California Thrasher - Desert Cottontail                           nonEst
##  California Thrasher - Desert Iguana                               nonEst
##  California Thrasher - Giant Kangaroo Rat                          nonEst
##  California Thrasher - Great White Egret                           nonEst
##  California Thrasher - Greater Roadrunner                          nonEst
##  California Thrasher - Heermann's Kangaroo Rat                     nonEst
##  California Thrasher - Horned Lark                                 nonEst
##  California Thrasher - Killdeer                                    nonEst
##  California Thrasher - Kit Fox                                     nonEst
##  California Thrasher - Lark Sparrow                                nonEst
##  California Thrasher - Loggerhead Shrike                           nonEst
##  California Thrasher - Merriam's Kangaroo Rat                      nonEst
##  California Thrasher - Mohave Ground Squirrel                      nonEst
##  California Thrasher - Mourning Dove                               nonEst
##  California Thrasher - Nelson's Antelope Squirrel                  nonEst
##  California Thrasher - (Red-tailed Hawk)                           nonEst
##  California Thrasher - Salinas Pocket Mouse                        nonEst
##  California Thrasher - Say's Phoebe                                nonEst
##  California Thrasher - Vesper Sparrow                              nonEst
##  California Thrasher - Western whiptail                            nonEst
##  California Thrasher - (White-tailed Antelope Squirrel)            nonEst
##  Common Raven - Coyote                                            -0.5034
##  Common Raven - Desert Cottontail                                  0.2513
##  Common Raven - Desert Iguana                                      nonEst
##  Common Raven - Giant Kangaroo Rat                                 0.1749
##  Common Raven - Great White Egret                                  nonEst
##  Common Raven - Greater Roadrunner                                 2.4485
##  Common Raven - Heermann's Kangaroo Rat                           -3.3875
##  Common Raven - Horned Lark                                        nonEst
##  Common Raven - Killdeer                                           nonEst
##  Common Raven - Kit Fox                                            1.6864
##  Common Raven - Lark Sparrow                                       2.4485
##  Common Raven - Loggerhead Shrike                                  1.8295
##  Common Raven - Merriam's Kangaroo Rat                             1.8295
##  Common Raven - Mohave Ground Squirrel                             nonEst
##  Common Raven - Mourning Dove                                      2.7850
##  Common Raven - Nelson's Antelope Squirrel                        -0.7472
##  Common Raven - (Red-tailed Hawk)                                  nonEst
##  Common Raven - Salinas Pocket Mouse                               3.0082
##  Common Raven - Say's Phoebe                                       3.0082
##  Common Raven - Vesper Sparrow                                     1.8295
##  Common Raven - Western whiptail                                   nonEst
##  Common Raven - (White-tailed Antelope Squirrel)                   2.6027
##  Coyote - Desert Cottontail                                        0.7547
##  Coyote - Desert Iguana                                            nonEst
##  Coyote - Giant Kangaroo Rat                                       0.6783
##  Coyote - Great White Egret                                        nonEst
##  Coyote - Greater Roadrunner                                       2.9519
##  Coyote - Heermann's Kangaroo Rat                                 -2.8841
##  Coyote - Horned Lark                                              nonEst
##  Coyote - Killdeer                                                 nonEst
##  Coyote - Kit Fox                                                  2.1898
##  Coyote - Lark Sparrow                                             2.9519
##  Coyote - Loggerhead Shrike                                        2.3329
##  Coyote - Merriam's Kangaroo Rat                                   2.3329
##  Coyote - Mohave Ground Squirrel                                   nonEst
##  Coyote - Mourning Dove                                            3.2884
##  Coyote - Nelson's Antelope Squirrel                              -0.2438
##  Coyote - (Red-tailed Hawk)                                        nonEst
##  Coyote - Salinas Pocket Mouse                                     3.5115
##  Coyote - Say's Phoebe                                             3.5115
##  Coyote - Vesper Sparrow                                           2.3329
##  Coyote - Western whiptail                                         nonEst
##  Coyote - (White-tailed Antelope Squirrel)                         3.1061
##  Desert Cottontail - Desert Iguana                                 nonEst
##  Desert Cottontail - Giant Kangaroo Rat                           -0.0764
##  Desert Cottontail - Great White Egret                             nonEst
##  Desert Cottontail - Greater Roadrunner                            2.1972
##  Desert Cottontail - Heermann's Kangaroo Rat                      -3.6388
##  Desert Cottontail - Horned Lark                                   nonEst
##  Desert Cottontail - Killdeer                                      nonEst
##  Desert Cottontail - Kit Fox                                       1.4351
##  Desert Cottontail - Lark Sparrow                                  2.1972
##  Desert Cottontail - Loggerhead Shrike                             1.5782
##  Desert Cottontail - Merriam's Kangaroo Rat                        1.5782
##  Desert Cottontail - Mohave Ground Squirrel                        nonEst
##  Desert Cottontail - Mourning Dove                                 2.5337
##  Desert Cottontail - Nelson's Antelope Squirrel                   -0.9985
##  Desert Cottontail - (Red-tailed Hawk)                             nonEst
##  Desert Cottontail - Salinas Pocket Mouse                          2.7568
##  Desert Cottontail - Say's Phoebe                                  2.7568
##  Desert Cottontail - Vesper Sparrow                                1.5782
##  Desert Cottontail - Western whiptail                              nonEst
##  Desert Cottontail - (White-tailed Antelope Squirrel)              2.3514
##  Desert Iguana - Giant Kangaroo Rat                                nonEst
##  Desert Iguana - Great White Egret                                 nonEst
##  Desert Iguana - Greater Roadrunner                                nonEst
##  Desert Iguana - Heermann's Kangaroo Rat                           nonEst
##  Desert Iguana - Horned Lark                                       nonEst
##  Desert Iguana - Killdeer                                          nonEst
##  Desert Iguana - Kit Fox                                           nonEst
##  Desert Iguana - Lark Sparrow                                      nonEst
##  Desert Iguana - Loggerhead Shrike                                 nonEst
##  Desert Iguana - Merriam's Kangaroo Rat                            nonEst
##  Desert Iguana - Mohave Ground Squirrel                            nonEst
##  Desert Iguana - Mourning Dove                                     nonEst
##  Desert Iguana - Nelson's Antelope Squirrel                        nonEst
##  Desert Iguana - (Red-tailed Hawk)                                 nonEst
##  Desert Iguana - Salinas Pocket Mouse                              nonEst
##  Desert Iguana - Say's Phoebe                                      nonEst
##  Desert Iguana - Vesper Sparrow                                    nonEst
##  Desert Iguana - Western whiptail                                  nonEst
##  Desert Iguana - (White-tailed Antelope Squirrel)                  nonEst
##  Giant Kangaroo Rat - Great White Egret                            nonEst
##  Giant Kangaroo Rat - Greater Roadrunner                           2.2736
##  Giant Kangaroo Rat - Heermann's Kangaroo Rat                     -3.5625
##  Giant Kangaroo Rat - Horned Lark                                  nonEst
##  Giant Kangaroo Rat - Killdeer                                     nonEst
##  Giant Kangaroo Rat - Kit Fox                                      1.5115
##  Giant Kangaroo Rat - Lark Sparrow                                 2.2736
##  Giant Kangaroo Rat - Loggerhead Shrike                            1.6546
##  Giant Kangaroo Rat - Merriam's Kangaroo Rat                       1.6546
##  Giant Kangaroo Rat - Mohave Ground Squirrel                       nonEst
##  Giant Kangaroo Rat - Mourning Dove                                2.6101
##  Giant Kangaroo Rat - Nelson's Antelope Squirrel                  -0.9222
##  Giant Kangaroo Rat - (Red-tailed Hawk)                            nonEst
##  Giant Kangaroo Rat - Salinas Pocket Mouse                         2.8332
##  Giant Kangaroo Rat - Say's Phoebe                                 2.8332
##  Giant Kangaroo Rat - Vesper Sparrow                               1.6546
##  Giant Kangaroo Rat - Western whiptail                             nonEst
##  Giant Kangaroo Rat - (White-tailed Antelope Squirrel)             2.4277
##  Great White Egret - Greater Roadrunner                            nonEst
##  Great White Egret - Heermann's Kangaroo Rat                       nonEst
##  Great White Egret - Horned Lark                                   nonEst
##  Great White Egret - Killdeer                                      nonEst
##  Great White Egret - Kit Fox                                       nonEst
##  Great White Egret - Lark Sparrow                                  nonEst
##  Great White Egret - Loggerhead Shrike                             nonEst
##  Great White Egret - Merriam's Kangaroo Rat                        nonEst
##  Great White Egret - Mohave Ground Squirrel                        nonEst
##  Great White Egret - Mourning Dove                                 nonEst
##  Great White Egret - Nelson's Antelope Squirrel                    nonEst
##  Great White Egret - (Red-tailed Hawk)                             nonEst
##  Great White Egret - Salinas Pocket Mouse                          nonEst
##  Great White Egret - Say's Phoebe                                  nonEst
##  Great White Egret - Vesper Sparrow                                nonEst
##  Great White Egret - Western whiptail                              nonEst
##  Great White Egret - (White-tailed Antelope Squirrel)              nonEst
##  Greater Roadrunner - Heermann's Kangaroo Rat                     -5.8361
##  Greater Roadrunner - Horned Lark                                  nonEst
##  Greater Roadrunner - Killdeer                                     nonEst
##  Greater Roadrunner - Kit Fox                                     -0.7621
##  Greater Roadrunner - Lark Sparrow                                 0.0000
##  Greater Roadrunner - Loggerhead Shrike                           -0.6190
##  Greater Roadrunner - Merriam's Kangaroo Rat                      -0.6190
##  Greater Roadrunner - Mohave Ground Squirrel                       nonEst
##  Greater Roadrunner - Mourning Dove                                0.3365
##  Greater Roadrunner - Nelson's Antelope Squirrel                  -3.1958
##  Greater Roadrunner - (Red-tailed Hawk)                            nonEst
##  Greater Roadrunner - Salinas Pocket Mouse                         0.5596
##  Greater Roadrunner - Say's Phoebe                                 0.5596
##  Greater Roadrunner - Vesper Sparrow                              -0.6190
##  Greater Roadrunner - Western whiptail                             nonEst
##  Greater Roadrunner - (White-tailed Antelope Squirrel)             0.1542
##  Heermann's Kangaroo Rat - Horned Lark                             nonEst
##  Heermann's Kangaroo Rat - Killdeer                                nonEst
##  Heermann's Kangaroo Rat - Kit Fox                                 5.0739
##  Heermann's Kangaroo Rat - Lark Sparrow                            5.8361
##  Heermann's Kangaroo Rat - Loggerhead Shrike                       5.2170
##  Heermann's Kangaroo Rat - Merriam's Kangaroo Rat                  5.2170
##  Heermann's Kangaroo Rat - Mohave Ground Squirrel                  nonEst
##  Heermann's Kangaroo Rat - Mourning Dove                           6.1725
##  Heermann's Kangaroo Rat - Nelson's Antelope Squirrel              2.6403
##  Heermann's Kangaroo Rat - (Red-tailed Hawk)                       nonEst
##  Heermann's Kangaroo Rat - Salinas Pocket Mouse                    6.3957
##  Heermann's Kangaroo Rat - Say's Phoebe                            6.3957
##  Heermann's Kangaroo Rat - Vesper Sparrow                          5.2170
##  Heermann's Kangaroo Rat - Western whiptail                        nonEst
##  Heermann's Kangaroo Rat - (White-tailed Antelope Squirrel)        5.9902
##  Horned Lark - Killdeer                                            nonEst
##  Horned Lark - Kit Fox                                             nonEst
##  Horned Lark - Lark Sparrow                                        nonEst
##  Horned Lark - Loggerhead Shrike                                   nonEst
##  Horned Lark - Merriam's Kangaroo Rat                              nonEst
##  Horned Lark - Mohave Ground Squirrel                              nonEst
##  Horned Lark - Mourning Dove                                       nonEst
##  Horned Lark - Nelson's Antelope Squirrel                          nonEst
##  Horned Lark - (Red-tailed Hawk)                                   nonEst
##  Horned Lark - Salinas Pocket Mouse                                nonEst
##  Horned Lark - Say's Phoebe                                        nonEst
##  Horned Lark - Vesper Sparrow                                      nonEst
##  Horned Lark - Western whiptail                                    nonEst
##  Horned Lark - (White-tailed Antelope Squirrel)                    nonEst
##  Killdeer - Kit Fox                                                nonEst
##  Killdeer - Lark Sparrow                                           nonEst
##  Killdeer - Loggerhead Shrike                                      nonEst
##  Killdeer - Merriam's Kangaroo Rat                                 nonEst
##  Killdeer - Mohave Ground Squirrel                                 nonEst
##  Killdeer - Mourning Dove                                          nonEst
##  Killdeer - Nelson's Antelope Squirrel                             nonEst
##  Killdeer - (Red-tailed Hawk)                                      nonEst
##  Killdeer - Salinas Pocket Mouse                                   nonEst
##  Killdeer - Say's Phoebe                                           nonEst
##  Killdeer - Vesper Sparrow                                         nonEst
##  Killdeer - Western whiptail                                       nonEst
##  Killdeer - (White-tailed Antelope Squirrel)                       nonEst
##  Kit Fox - Lark Sparrow                                            0.7621
##  Kit Fox - Loggerhead Shrike                                       0.1431
##  Kit Fox - Merriam's Kangaroo Rat                                  0.1431
##  Kit Fox - Mohave Ground Squirrel                                  nonEst
##  Kit Fox - Mourning Dove                                           1.0986
##  Kit Fox - Nelson's Antelope Squirrel                             -2.4336
##  Kit Fox - (Red-tailed Hawk)                                       nonEst
##  Kit Fox - Salinas Pocket Mouse                                    1.3218
##  Kit Fox - Say's Phoebe                                            1.3218
##  Kit Fox - Vesper Sparrow                                          0.1431
##  Kit Fox - Western whiptail                                        nonEst
##  Kit Fox - (White-tailed Antelope Squirrel)                        0.9163
##  Lark Sparrow - Loggerhead Shrike                                 -0.6190
##  Lark Sparrow - Merriam's Kangaroo Rat                            -0.6190
##  Lark Sparrow - Mohave Ground Squirrel                             nonEst
##  Lark Sparrow - Mourning Dove                                      0.3365
##  Lark Sparrow - Nelson's Antelope Squirrel                        -3.1958
##  Lark Sparrow - (Red-tailed Hawk)                                  nonEst
##  Lark Sparrow - Salinas Pocket Mouse                               0.5596
##  Lark Sparrow - Say's Phoebe                                       0.5596
##  Lark Sparrow - Vesper Sparrow                                    -0.6190
##  Lark Sparrow - Western whiptail                                   nonEst
##  Lark Sparrow - (White-tailed Antelope Squirrel)                   0.1542
##  Loggerhead Shrike - Merriam's Kangaroo Rat                        0.0000
##  Loggerhead Shrike - Mohave Ground Squirrel                        nonEst
##  Loggerhead Shrike - Mourning Dove                                 0.9555
##  Loggerhead Shrike - Nelson's Antelope Squirrel                   -2.5767
##  Loggerhead Shrike - (Red-tailed Hawk)                             nonEst
##  Loggerhead Shrike - Salinas Pocket Mouse                          1.1787
##  Loggerhead Shrike - Say's Phoebe                                  1.1787
##  Loggerhead Shrike - Vesper Sparrow                                0.0000
##  Loggerhead Shrike - Western whiptail                              nonEst
##  Loggerhead Shrike - (White-tailed Antelope Squirrel)              0.7732
##  Merriam's Kangaroo Rat - Mohave Ground Squirrel                   nonEst
##  Merriam's Kangaroo Rat - Mourning Dove                            0.9555
##  Merriam's Kangaroo Rat - Nelson's Antelope Squirrel              -2.5767
##  Merriam's Kangaroo Rat - (Red-tailed Hawk)                        nonEst
##  Merriam's Kangaroo Rat - Salinas Pocket Mouse                     1.1787
##  Merriam's Kangaroo Rat - Say's Phoebe                             1.1787
##  Merriam's Kangaroo Rat - Vesper Sparrow                           0.0000
##  Merriam's Kangaroo Rat - Western whiptail                         nonEst
##  Merriam's Kangaroo Rat - (White-tailed Antelope Squirrel)         0.7732
##  Mohave Ground Squirrel - Mourning Dove                            nonEst
##  Mohave Ground Squirrel - Nelson's Antelope Squirrel               nonEst
##  Mohave Ground Squirrel - (Red-tailed Hawk)                        nonEst
##  Mohave Ground Squirrel - Salinas Pocket Mouse                     nonEst
##  Mohave Ground Squirrel - Say's Phoebe                             nonEst
##  Mohave Ground Squirrel - Vesper Sparrow                           nonEst
##  Mohave Ground Squirrel - Western whiptail                         nonEst
##  Mohave Ground Squirrel - (White-tailed Antelope Squirrel)         nonEst
##  Mourning Dove - Nelson's Antelope Squirrel                       -3.5322
##  Mourning Dove - (Red-tailed Hawk)                                 nonEst
##  Mourning Dove - Salinas Pocket Mouse                              0.2231
##  Mourning Dove - Say's Phoebe                                      0.2231
##  Mourning Dove - Vesper Sparrow                                   -0.9555
##  Mourning Dove - Western whiptail                                  nonEst
##  Mourning Dove - (White-tailed Antelope Squirrel)                 -0.1823
##  Nelson's Antelope Squirrel - (Red-tailed Hawk)                    nonEst
##  Nelson's Antelope Squirrel - Salinas Pocket Mouse                 3.7554
##  Nelson's Antelope Squirrel - Say's Phoebe                         3.7554
##  Nelson's Antelope Squirrel - Vesper Sparrow                       2.5767
##  Nelson's Antelope Squirrel - Western whiptail                     nonEst
##  Nelson's Antelope Squirrel - (White-tailed Antelope Squirrel)     3.3499
##  (Red-tailed Hawk) - Salinas Pocket Mouse                          nonEst
##  (Red-tailed Hawk) - Say's Phoebe                                  nonEst
##  (Red-tailed Hawk) - Vesper Sparrow                                nonEst
##  (Red-tailed Hawk) - Western whiptail                              nonEst
##  (Red-tailed Hawk) - (White-tailed Antelope Squirrel)              nonEst
##  Salinas Pocket Mouse - Say's Phoebe                               0.0000
##  Salinas Pocket Mouse - Vesper Sparrow                            -1.1787
##  Salinas Pocket Mouse - Western whiptail                           nonEst
##  Salinas Pocket Mouse - (White-tailed Antelope Squirrel)          -0.4055
##  Say's Phoebe - Vesper Sparrow                                    -1.1787
##  Say's Phoebe - Western whiptail                                   nonEst
##  Say's Phoebe - (White-tailed Antelope Squirrel)                  -0.4055
##  Vesper Sparrow - Western whiptail                                 nonEst
##  Vesper Sparrow - (White-tailed Antelope Squirrel)                 0.7732
##  Western whiptail - (White-tailed Antelope Squirrel)               nonEst
##      SE  df z.ratio p.value
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3555 Inf  12.633  <.0001
##  0.2911 Inf  14.037  <.0001
##  0.1755 Inf  17.344  <.0001
##  0.0596 Inf   7.228  <.0001
##  0.3555 Inf  12.633  <.0001
##  0.1436 Inf  18.238  <.0001
##      NA  NA      NA      NA
##  0.0870 Inf  17.044  <.0001
##  0.0716 Inf  13.679  <.0001
##  0.0966 Inf  17.951  <.0001
##      NA  NA      NA      NA
##  0.0936 Inf  17.724  <.0001
##      NA  NA      NA      NA
##  0.2699 Inf  14.569  <.0001
##  0.0401 Inf -47.470  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.1864 Inf  17.007  <.0001
##  0.2699 Inf  14.569  <.0001
##  0.1997 Inf  16.593  <.0001
##  0.1997 Inf  16.593  <.0001
##      NA  NA      NA      NA
##  0.3184 Inf  13.404  <.0001
##  0.0658 Inf  11.193  <.0001
##      NA  NA      NA      NA
##  0.3555 Inf  12.633  <.0001
##  0.3555 Inf  12.633  <.0001
##  0.1997 Inf  16.593  <.0001
##      NA  NA      NA      NA
##  0.2911 Inf  14.037  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.4564 Inf  -0.888  1.0000
##  0.3930 Inf  -3.682  0.0729
##  0.3566 Inf -11.387  <.0001
##  0.5000 Inf   0.000  1.0000
##  0.3798 Inf  -4.929  0.0004
##      NA  NA      NA      NA
##  0.3622 Inf  -8.306  <.0001
##  0.3588 Inf  -9.787  <.0001
##  0.3646 Inf  -7.561  <.0001
##      NA  NA      NA      NA
##  0.3638 Inf  -7.788  <.0001
##      NA  NA      NA      NA
##  0.4432 Inf  -1.263  1.0000
##  0.3538 Inf -18.075  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3979 Inf  -3.322  0.2085
##  0.4432 Inf  -1.263  1.0000
##  0.4043 Inf  -2.915  0.4964
##  0.4043 Inf  -2.915  0.4964
##      NA  NA      NA      NA
##  0.4743 Inf  -0.470  1.0000
##  0.3577 Inf -10.500  <.0001
##      NA  NA      NA      NA
##  0.5000 Inf   0.000  1.0000
##  0.5000 Inf   0.000  1.0000
##  0.4043 Inf  -2.915  0.4964
##      NA  NA      NA      NA
##  0.4564 Inf  -0.888  1.0000
##  0.3358 Inf  -3.102  0.3488
##  0.2924 Inf -12.501  <.0001
##  0.4564 Inf   0.888  1.0000
##  0.3203 Inf  -4.579  0.0021
##      NA  NA      NA      NA
##  0.2992 Inf  -8.700  <.0001
##  0.2951 Inf -10.527  <.0001
##  0.3021 Inf  -7.783  <.0001
##      NA  NA      NA      NA
##  0.3011 Inf  -8.062  <.0001
##      NA  NA      NA      NA
##  0.3934 Inf  -0.392  1.0000
##  0.2890 Inf -20.725  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3416 Inf  -2.683  0.6900
##  0.3934 Inf  -0.392  1.0000
##  0.3490 Inf  -2.216  0.9487
##  0.3490 Inf  -2.216  0.9487
##      NA  NA      NA      NA
##  0.4282 Inf   0.426  1.0000
##  0.2937 Inf -11.406  <.0001
##      NA  NA      NA      NA
##  0.4564 Inf   0.888  1.0000
##  0.4564 Inf   0.888  1.0000
##  0.3490 Inf  -2.216  0.9487
##      NA  NA      NA      NA
##  0.4082 Inf   0.000  1.0000
##  0.1777 Inf -14.710  <.0001
##  0.3930 Inf   3.682  0.0729
##  0.2206 Inf  -1.926  0.9926
##      NA  NA      NA      NA
##  0.1886 Inf  -8.276  <.0001
##  0.1821 Inf -11.341  <.0001
##  0.1933 Inf  -6.778  <.0001
##      NA  NA      NA      NA
##  0.1917 Inf  -7.230  <.0001
##      NA  NA      NA      NA
##  0.3176 Inf   2.794  0.5984
##  0.1721 Inf -28.754  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2505 Inf   0.500  1.0000
##  0.3176 Inf   2.794  0.5984
##  0.2605 Inf   1.030  1.0000
##  0.2605 Inf   1.030  1.0000
##      NA  NA      NA      NA
##  0.3597 Inf   3.402  0.1687
##  0.1798 Inf -12.837  <.0001
##      NA  NA      NA      NA
##  0.3930 Inf   3.682  0.0729
##  0.3930 Inf   3.682  0.0729
##  0.2605 Inf   1.030  1.0000
##      NA  NA      NA      NA
##  0.3358 Inf   3.102  0.3488
##  0.3566 Inf  11.387  <.0001
##  0.1462 Inf  14.966  <.0001
##      NA  NA      NA      NA
##  0.0913 Inf  11.531  <.0001
##  0.0767 Inf   7.154  <.0001
##  0.1005 Inf  12.977  <.0001
##      NA  NA      NA      NA
##  0.0975 Inf  12.586  <.0001
##      NA  NA      NA      NA
##  0.2713 Inf  12.906  <.0001
##  0.0486 Inf -48.032  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.1884 Inf  14.538  <.0001
##  0.2713 Inf  12.906  <.0001
##  0.2015 Inf  14.299  <.0001
##  0.2015 Inf  14.299  <.0001
##      NA  NA      NA      NA
##  0.3196 Inf  12.006  <.0001
##  0.0713 Inf   4.281  0.0079
##      NA  NA      NA      NA
##  0.3566 Inf  11.387  <.0001
##  0.3566 Inf  11.387  <.0001
##  0.2015 Inf  14.299  <.0001
##      NA  NA      NA      NA
##  0.2924 Inf  12.501  <.0001
##  0.3798 Inf  -4.929  0.0004
##      NA  NA      NA      NA
##  0.3622 Inf  -8.306  <.0001
##  0.3588 Inf  -9.787  <.0001
##  0.3646 Inf  -7.561  <.0001
##      NA  NA      NA      NA
##  0.3638 Inf  -7.788  <.0001
##      NA  NA      NA      NA
##  0.4432 Inf  -1.263  1.0000
##  0.3538 Inf -18.075  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3979 Inf  -3.322  0.2085
##  0.4432 Inf  -1.263  1.0000
##  0.4043 Inf  -2.915  0.4964
##  0.4043 Inf  -2.915  0.4964
##      NA  NA      NA      NA
##  0.4743 Inf  -0.470  1.0000
##  0.3577 Inf -10.500  <.0001
##      NA  NA      NA      NA
##  0.5000 Inf   0.000  1.0000
##  0.5000 Inf   0.000  1.0000
##  0.4043 Inf  -2.915  0.4964
##      NA  NA      NA      NA
##  0.4564 Inf  -0.888  1.0000
##      NA  NA      NA      NA
##  0.1594 Inf  -7.130  <.0001
##  0.1515 Inf -10.821  <.0001
##  0.1648 Inf  -5.370  <.0001
##      NA  NA      NA      NA
##  0.1630 Inf  -5.897  <.0001
##      NA  NA      NA      NA
##  0.3011 Inf   4.358  0.0057
##  0.1394 Inf -32.447  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2293 Inf   2.399  0.8778
##  0.3011 Inf   4.358  0.0057
##  0.2402 Inf   2.886  0.5211
##  0.2402 Inf   2.886  0.5211
##      NA  NA      NA      NA
##  0.3453 Inf   4.775  0.0009
##  0.1488 Inf -12.655  <.0001
##      NA  NA      NA      NA
##  0.3798 Inf   4.929  0.0004
##  0.3798 Inf   4.929  0.0004
##  0.2402 Inf   2.886  0.5211
##      NA  NA      NA      NA
##  0.3203 Inf   4.579  0.0021
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.0995 Inf  -5.058  0.0002
##  0.1188 Inf   2.116  0.9713
##      NA  NA      NA      NA
##  0.1163 Inf   1.504  0.9999
##      NA  NA      NA      NA
##  0.2786 Inf   8.790  <.0001
##  0.0799 Inf -42.406  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.1988 Inf   8.485  <.0001
##  0.2786 Inf   8.790  <.0001
##  0.2113 Inf   8.660  <.0001
##  0.2113 Inf   8.660  <.0001
##      NA  NA      NA      NA
##  0.3258 Inf   8.547  <.0001
##  0.0954 Inf  -7.834  <.0001
##      NA  NA      NA      NA
##  0.3622 Inf   8.306  <.0001
##  0.3622 Inf   8.306  <.0001
##  0.2113 Inf   8.660  <.0001
##      NA  NA      NA      NA
##  0.2992 Inf   8.700  <.0001
##  0.1080 Inf   6.987  <.0001
##      NA  NA      NA      NA
##  0.1053 Inf   6.443  <.0001
##      NA  NA      NA      NA
##  0.2742 Inf  10.767  <.0001
##  0.0628 Inf -45.948  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.1925 Inf  11.374  <.0001
##  0.2742 Inf  10.767  <.0001
##  0.2054 Inf  11.357  <.0001
##  0.2054 Inf  11.357  <.0001
##      NA  NA      NA      NA
##  0.3221 Inf  10.210  <.0001
##  0.0816 Inf  -2.989  0.4360
##      NA  NA      NA      NA
##  0.3588 Inf   9.787  <.0001
##  0.3588 Inf   9.787  <.0001
##  0.2054 Inf  11.357  <.0001
##      NA  NA      NA      NA
##  0.2951 Inf  10.527  <.0001
##      NA  NA      NA      NA
##  0.1237 Inf  -0.618  1.0000
##      NA  NA      NA      NA
##  0.2817 Inf   7.799  <.0001
##  0.0903 Inf -40.319  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2031 Inf   7.064  <.0001
##  0.2817 Inf   7.799  <.0001
##  0.2154 Inf   7.327  <.0001
##  0.2154 Inf   7.327  <.0001
##      NA  NA      NA      NA
##  0.3285 Inf   7.712  <.0001
##  0.1042 Inf  -9.582  <.0001
##      NA  NA      NA      NA
##  0.3646 Inf   7.561  <.0001
##  0.3646 Inf   7.561  <.0001
##  0.2154 Inf   7.327  <.0001
##      NA  NA      NA      NA
##  0.3021 Inf   7.783  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2807 Inf   8.100  <.0001
##  0.0870 Inf -40.968  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2017 Inf   7.493  <.0001
##  0.2807 Inf   8.100  <.0001
##  0.2140 Inf   7.730  <.0001
##  0.2140 Inf   7.730  <.0001
##      NA  NA      NA      NA
##  0.3276 Inf   7.966  <.0001
##  0.1014 Inf  -9.096  <.0001
##      NA  NA      NA      NA
##  0.3638 Inf   7.788  <.0001
##  0.3638 Inf   7.788  <.0001
##  0.2140 Inf   7.730  <.0001
##      NA  NA      NA      NA
##  0.3011 Inf   8.062  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.2677 Inf -21.805  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3237 Inf  -2.355  0.8988
##  0.3780 Inf   0.000  1.0000
##  0.3315 Inf  -1.867  0.9955
##  0.3315 Inf  -1.867  0.9955
##      NA  NA      NA      NA
##  0.4140 Inf   0.813  1.0000
##  0.2727 Inf -11.720  <.0001
##      NA  NA      NA      NA
##  0.4432 Inf   1.263  1.0000
##  0.4432 Inf   1.263  1.0000
##  0.3315 Inf  -1.867  0.9955
##      NA  NA      NA      NA
##  0.3934 Inf   0.392  1.0000
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.1831 Inf  27.704  <.0001
##  0.2677 Inf  21.805  <.0001
##  0.1966 Inf  26.530  <.0001
##  0.1966 Inf  26.530  <.0001
##      NA  NA      NA      NA
##  0.3166 Inf  19.499  <.0001
##  0.0560 Inf  47.174  <.0001
##      NA  NA      NA      NA
##  0.3538 Inf  18.075  <.0001
##  0.3538 Inf  18.075  <.0001
##  0.1966 Inf  26.530  <.0001
##      NA  NA      NA      NA
##  0.2890 Inf  20.725  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3237 Inf   2.355  0.8988
##  0.2679 Inf   0.534  1.0000
##  0.2679 Inf   0.534  1.0000
##      NA  NA      NA      NA
##  0.3651 Inf   3.009  0.4201
##  0.1904 Inf -12.781  <.0001
##      NA  NA      NA      NA
##  0.3979 Inf   3.322  0.2085
##  0.3979 Inf   3.322  0.2085
##  0.2679 Inf   0.534  1.0000
##      NA  NA      NA      NA
##  0.3416 Inf   2.683  0.6900
##  0.3315 Inf  -1.867  0.9955
##  0.3315 Inf  -1.867  0.9955
##      NA  NA      NA      NA
##  0.4140 Inf   0.813  1.0000
##  0.2727 Inf -11.720  <.0001
##      NA  NA      NA      NA
##  0.4432 Inf   1.263  1.0000
##  0.4432 Inf   1.263  1.0000
##  0.3315 Inf  -1.867  0.9955
##      NA  NA      NA      NA
##  0.3934 Inf   0.392  1.0000
##  0.2774 Inf   0.000  1.0000
##      NA  NA      NA      NA
##  0.3721 Inf   2.568  0.7761
##  0.2034 Inf -12.666  <.0001
##      NA  NA      NA      NA
##  0.4043 Inf   2.915  0.4964
##  0.4043 Inf   2.915  0.4964
##  0.2774 Inf   0.000  1.0000
##      NA  NA      NA      NA
##  0.3490 Inf   2.216  0.9487
##      NA  NA      NA      NA
##  0.3721 Inf   2.568  0.7761
##  0.2034 Inf -12.666  <.0001
##      NA  NA      NA      NA
##  0.4043 Inf   2.915  0.4964
##  0.4043 Inf   2.915  0.4964
##  0.2774 Inf   0.000  1.0000
##      NA  NA      NA      NA
##  0.3490 Inf   2.216  0.9487
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.3208 Inf -11.010  <.0001
##      NA  NA      NA      NA
##  0.4743 Inf   0.470  1.0000
##  0.4743 Inf   0.470  1.0000
##  0.3721 Inf  -2.568  0.7761
##      NA  NA      NA      NA
##  0.4282 Inf  -0.426  1.0000
##      NA  NA      NA      NA
##  0.3577 Inf  10.500  <.0001
##  0.3577 Inf  10.500  <.0001
##  0.2034 Inf  12.666  <.0001
##      NA  NA      NA      NA
##  0.2937 Inf  11.406  <.0001
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##      NA  NA      NA      NA
##  0.5000 Inf   0.000  1.0000
##  0.4043 Inf  -2.915  0.4964
##      NA  NA      NA      NA
##  0.4564 Inf  -0.888  1.0000
##  0.4043 Inf  -2.915  0.4964
##      NA  NA      NA      NA
##  0.4564 Inf  -0.888  1.0000
##      NA  NA      NA      NA
##  0.3490 Inf   2.216  0.9487
##      NA  NA      NA      NA
## 
## Results are averaged over the levels of: microsite 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 33 estimates
animals_density_final <- photo_final %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density_final <- animals_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "Mammal")
pca_data_final <- animals_density_final ### Created new df for pca data
pca_data_final <- pca_data_final %>%
  spread(common_name, captures) %>%
  ungroup() %>%
  dplyr::select(-site_code, -microsite, -plot) %>%
  replace(is.na(.),0)
dim(pca_data_final)
## [1] 24 35
env_final <- read.csv("environment_final.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env_final)
## [1] 24  5
model010 <- adonis(pca_data_final ~ microsite*shrub_density, data = env_final)
## 'adonis' will be deprecated: use 'adonis2' instead
model010
## $aov.tab
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##               Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)  
## microsite      1    0.2680 0.26799  1.0165 0.04244  0.376  
## shrub_density  1    0.5102 0.51024  1.9354 0.08081  0.071 .
## Residuals     21    5.5363 0.26363         0.87675         
## Total         23    6.3145                 1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $call
## adonis(formula = pca_data_final ~ microsite * shrub_density, 
##     data = env_final)
## 
## $coefficients
##                          shrub_density American Robin Black-tailed Jackrabbit
## (Intercept)                 -10.557692   4.166667e-02              -11.346154
## microsite1                  -14.057692  -4.166667e-02              -22.012821
## shrub_density                 2.876923   9.476236e-19                4.661538
## microsite1:shrub_density            NA             NA                      NA
##                          Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept)                        4.166667e-02                  1.1185897
## microsite1                        -4.166667e-02                  1.0352564
## shrub_density                      4.564605e-18                 -0.1692308
## microsite1:shrub_density                     NA                         NA
##                              Bobcat Brewer's Blackbird
## (Intercept)              -0.7019231         -2.4070513
## microsite1               -1.1185897         -2.8237179
## shrub_density             0.1692308          0.5538462
## microsite1:shrub_density         NA                 NA
##                          California Ground Squirrel California Pocket Mouse
## (Intercept)                               19.618590              0.33974359
## microsite1                                13.868590              0.17307692
## shrub_density                             -1.769231             -0.03076923
## microsite1:shrub_density                         NA                      NA
##                          California Quail California Thrasher Common Raven
## (Intercept)                     -6.185897                -1.0    2.5961538
## microsite1                      -7.352564                -1.0   -1.9038462
## shrub_density                    1.292308                 0.2    0.1384615
## microsite1:shrub_density               NA                  NA           NA
##                              Coyote Desert Cottontail Desert Iguana
## (Intercept)               3.2467949        -27.230769  1.958333e+00
## microsite1               -2.8365385        -28.897436 -1.958333e+00
## shrub_density             0.4153846          5.307692  3.783904e-16
## microsite1:shrub_density         NA                NA            NA
##                          Giant Kangaroo Rat Great White Egret
## (Intercept)                       6.8141026      4.166667e-02
## microsite1                        4.6474359     -4.166667e-02
## shrub_density                    -0.7076923      4.564605e-18
## microsite1:shrub_density                 NA                NA
##                          Greater Roadrunner Heermann's Kangaroo Rat Horned Lark
## (Intercept)                      -4.3814103              125.403846  0.38461538
## microsite1                       -4.4647436               50.903846  0.38461538
## shrub_density                     0.8307692               -4.538462 -0.04615385
## microsite1:shrub_density                 NA                      NA          NA
##                               Killdeer    Kit Fox Lark Sparrow
## (Intercept)               4.166667e-02  2.5288462   -1.8717949
## microsite1               -4.166667e-02  2.1121795   -2.2051282
## shrub_density             4.564605e-18 -0.3384615    0.3846154
## microsite1:shrub_density            NA         NA           NA
##                          Loggerhead Shrike Merriam's Kangaroo Rat
## (Intercept)                     -1.1025641             1.06089744
## microsite1                      -1.4358974             0.14423077
## shrub_density                    0.2923077            -0.09230769
## microsite1:shrub_density                NA                     NA
##                          Mohave Ground Squirrel Mourning Dove
## (Intercept)                        2.916667e-01    -1.9551282
## microsite1                        -2.916667e-01    -2.1217949
## shrub_density                     -1.570024e-16     0.3846154
## microsite1:shrub_density                     NA            NA
##                          Nelson's Antelope Squirrel No CV Result
## (Intercept)                               18.894231   -0.8685897
## microsite1                                13.977564   -0.9519231
## shrub_density                             -2.092308    0.1692308
## microsite1:shrub_density                         NA           NA
##                          Red-tailed Hawk Salinas Pocket Mouse Say's Phoebe
## (Intercept)                   -0.5641026          -0.35256410  0.253205128
## microsite1                    -0.5641026          -0.51923077  0.003205128
## shrub_density                  0.1076923           0.09230769 -0.015384615
## microsite1:shrub_density              NA                   NA           NA
##                          Vesper Sparrow Western whiptail
## (Intercept)                   1.5801282       0.12820513
## microsite1                    1.4967949       0.12820513
## shrub_density                -0.1846154      -0.01538462
## microsite1:shrub_density             NA               NA
##                          White-tailed Antelope Squirrel
## (Intercept)                                  0.33653846
## microsite1                                  -0.08012821
## shrub_density                               -0.01538462
## microsite1:shrub_density                             NA
## 
## $coef.sites
##                                    1           2           3           4
## (Intercept)               0.78080317  0.82434750  0.98306093  0.76605527
## microsite1                0.17504154  0.26134577  0.28849450  0.24337903
## shrub_density            -0.03080745 -0.04609079 -0.04138812 -0.03373393
## microsite1:shrub_density          NA          NA          NA          NA
##                                    5           6           7           8
## (Intercept)               0.84838652  0.83124969  0.86997845  0.76272593
## microsite1                0.30313950  0.28542834  0.29904628  0.20499025
## shrub_density            -0.05068255 -0.04597727 -0.04282439 -0.02976002
## microsite1:shrub_density          NA          NA          NA          NA
##                                   9         10          11         12
## (Intercept)               1.7481015  1.5203179  1.00167997  1.0125329
## microsite1                1.0946409  0.9430041  0.41975528  0.4948288
## shrub_density            -0.1923781 -0.1650103 -0.07142752 -0.0794049
## microsite1:shrub_density         NA         NA          NA         NA
##                                   13          14         15          16
## (Intercept)               0.88500919  0.84806327  0.8076157  0.83755826
## microsite1                0.18017241  0.07715532  0.2071152  0.27863228
## shrub_density            -0.03799694 -0.02089895 -0.0336203 -0.04242666
## microsite1:shrub_density          NA          NA         NA          NA
##                                   17          18          19          20
## (Intercept)               0.04881892  0.31671402  0.25149293  0.07189331
## microsite1               -0.82473427 -0.56994858 -0.63539881 -0.82608604
## shrub_density             0.13158888  0.08613734  0.09735222  0.13310018
## microsite1:shrub_density          NA          NA          NA          NA
##                                   21          22          23           24
## (Intercept)               1.12355908  1.11712532  1.06379973  0.830255162
## microsite1                0.41343026  0.46257310  0.31720419  0.079719825
## shrub_density            -0.06585209 -0.07418334 -0.04509589 -0.006831989
## microsite1:shrub_density          NA          NA          NA           NA
## 
## $f.perms
##               [,1]      [,2]
##    [1,] 0.42567613 1.6145440
##    [2,] 0.55164061 1.0902870
##    [3,] 2.11448261 1.7774341
##    [4,] 1.36365233 1.2634207
##    [5,] 0.77722767 0.3427893
##    [6,] 0.82030023 0.9880416
##    [7,] 0.38188850 0.5671524
##    [8,] 0.33887034 0.9687802
##    [9,] 0.41865359 0.1675109
##   [10,] 1.21971451 1.1376971
##   [11,] 0.55696033 0.6344934
##   [12,] 0.77840782 0.2585865
##   [13,] 0.39993694 1.6319336
##   [14,] 0.38387600 0.5104751
##   [15,] 0.70338421 0.5375679
##   [16,] 0.98799248 2.6060663
##   [17,] 0.68667064 0.6208726
##   [18,] 0.87453381 1.3576922
##   [19,] 0.67518963 0.6541362
##   [20,] 0.77324853 0.2665269
##   [21,] 0.77704580 1.0394374
##   [22,] 0.73599659 1.3133106
##   [23,] 0.23274360 0.4443959
##   [24,] 1.30336002 1.8999963
##   [25,] 1.16014174 0.4038810
##   [26,] 1.61473993 0.6060563
##   [27,] 1.58917631 0.8940414
##   [28,] 0.34141369 0.5217915
##   [29,] 0.27440040 1.2435496
##   [30,] 1.36481807 0.5366875
##   [31,] 1.76876216 0.6281006
##   [32,] 0.26026514 0.9878970
##   [33,] 2.70423838 1.0036309
##   [34,] 1.85116883 0.4887258
##   [35,] 1.64174717 0.8959147
##   [36,] 1.41627217 1.2222351
##   [37,] 1.06043960 3.3711944
##   [38,] 3.19918238 0.9379562
##   [39,] 0.62223733 1.3685439
##   [40,] 1.00443067 1.2619194
##   [41,] 0.29222755 0.1809151
##   [42,] 0.49577695 1.1309120
##   [43,] 0.63298788 0.5052355
##   [44,] 0.20842058 1.5482573
##   [45,] 1.02952960 0.2508530
##   [46,] 0.76120568 0.8428552
##   [47,] 0.97881139 2.8360015
##   [48,] 0.59632990 1.5377674
##   [49,] 0.73912879 0.7019683
##   [50,] 3.62777830 2.1098562
##   [51,] 0.67789490 1.0268624
##   [52,] 1.56635276 0.8085958
##   [53,] 1.07717630 1.4393057
##   [54,] 0.57284904 0.8766896
##   [55,] 1.47364908 0.5751834
##   [56,] 1.99679830 1.3605279
##   [57,] 0.46284768 0.5414056
##   [58,] 1.80964366 1.0383247
##   [59,] 0.43285539 0.6481075
##   [60,] 0.90260154 0.5838898
##   [61,] 0.81583441 0.8773683
##   [62,] 2.66610020 0.4439930
##   [63,] 1.52262596 0.3348183
##   [64,] 0.95908497 0.4279535
##   [65,] 0.83322189 0.9099560
##   [66,] 2.17042539 0.4409632
##   [67,] 0.98106601 0.2831945
##   [68,] 1.32170021 0.8207783
##   [69,] 1.35296578 0.3469620
##   [70,] 0.46540688 0.8476617
##   [71,] 0.92988068 0.6207447
##   [72,] 0.66447961 1.4579331
##   [73,] 0.59033775 1.5648165
##   [74,] 1.56707346 1.8551850
##   [75,] 1.08302663 0.8570190
##   [76,] 1.28780857 0.5925282
##   [77,] 0.47891287 1.4337722
##   [78,] 0.72002337 1.5214506
##   [79,] 0.82007662 1.1019993
##   [80,] 1.25132477 5.0408121
##   [81,] 0.57207088 0.2251390
##   [82,] 0.75058461 0.8654571
##   [83,] 1.06139808 1.2438681
##   [84,] 2.03029206 1.4827397
##   [85,] 1.20743015 1.1488845
##   [86,] 0.94166585 1.5039991
##   [87,] 0.34567549 0.4143180
##   [88,] 3.32316860 1.4847499
##   [89,] 0.97011360 1.1405347
##   [90,] 1.48016184 1.2849006
##   [91,] 0.53846658 1.8824596
##   [92,] 0.87245001 0.4209524
##   [93,] 2.75490749 0.4885455
##   [94,] 0.47310850 0.9284034
##   [95,] 1.66909260 1.0563591
##   [96,] 0.98590247 1.3061993
##   [97,] 0.61341665 0.5225113
##   [98,] 0.43471984 1.0161509
##   [99,] 0.78170409 0.5183545
##  [100,] 1.17846175 1.4244480
##  [101,] 3.14040139 1.2148783
##  [102,] 0.82253364 0.9332218
##  [103,] 1.03786706 1.2280901
##  [104,] 0.96841375 1.0440487
##  [105,] 1.02109055 0.8431252
##  [106,] 1.78561075 0.5405544
##  [107,] 0.43958912 0.5622185
##  [108,] 1.45605764 0.7206247
##  [109,] 0.38607550 1.3717749
##  [110,] 0.85132025 0.8787991
##  [111,] 0.99777197 1.1206987
##  [112,] 0.49657394 1.8428639
##  [113,] 2.77406004 1.2672374
##  [114,] 1.84414098 0.9328561
##  [115,] 0.32837891 0.7242991
##  [116,] 0.60293865 1.0657253
##  [117,] 3.10031847 1.1562892
##  [118,] 0.66070386 1.6633590
##  [119,] 0.39632827 1.2714468
##  [120,] 0.79697883 0.6175843
##  [121,] 3.04106898 1.3577498
##  [122,] 2.42038060 1.6231844
##  [123,] 0.45759492 0.4011322
##  [124,] 1.51959417 0.7126546
##  [125,] 0.48602956 0.4905166
##  [126,] 0.58136489 1.8686203
##  [127,] 1.03320747 0.4874074
##  [128,] 1.01303830 0.5042066
##  [129,] 0.33139955 0.5710786
##  [130,] 0.57022749 0.2781573
##  [131,] 0.46715634 1.0847039
##  [132,] 1.00581247 0.8984551
##  [133,] 0.31740570 0.2978362
##  [134,] 0.50637605 0.9242483
##  [135,] 4.91207832 0.5961972
##  [136,] 0.37728923 0.1759206
##  [137,] 0.23045599 0.9242226
##  [138,] 1.19776375 1.0393836
##  [139,] 0.47885280 1.4791075
##  [140,] 0.38346947 0.9248988
##  [141,] 1.17265242 0.8799809
##  [142,] 0.37691695 0.6131272
##  [143,] 0.39583478 0.4792491
##  [144,] 1.50591210 0.5281395
##  [145,] 1.99846747 1.3059075
##  [146,] 1.13521051 2.5073410
##  [147,] 1.46813929 0.8832800
##  [148,] 0.45426947 1.0309341
##  [149,] 0.62955311 0.6645688
##  [150,] 0.54952434 0.3568960
##  [151,] 0.65907984 1.0565734
##  [152,] 0.78490306 0.3501668
##  [153,] 1.40852470 4.8610226
##  [154,] 0.94346322 1.3021137
##  [155,] 0.35697777 0.7316048
##  [156,] 0.57503568 1.5090663
##  [157,] 2.61281366 0.2097528
##  [158,] 0.96943895 1.6850993
##  [159,] 0.47376456 0.4818232
##  [160,] 1.96506404 0.3202931
##  [161,] 0.33223973 0.6106234
##  [162,] 0.58890276 0.8652950
##  [163,] 0.56662228 0.2961035
##  [164,] 0.76447376 0.6709196
##  [165,] 1.54581751 0.9364870
##  [166,] 0.36154883 1.6331480
##  [167,] 0.95838167 0.5411279
##  [168,] 0.69875833 0.4383799
##  [169,] 4.43386721 1.5061332
##  [170,] 0.89413761 0.6801373
##  [171,] 0.85342761 1.2601183
##  [172,] 0.42038837 1.9451041
##  [173,] 0.90130159 0.8984389
##  [174,] 0.50168692 1.3888042
##  [175,] 0.37385337 0.2167949
##  [176,] 0.85037544 0.6323192
##  [177,] 0.91865711 0.6016669
##  [178,] 0.48835099 0.5529856
##  [179,] 0.66182963 1.2227483
##  [180,] 2.30062848 1.4615372
##  [181,] 0.52278928 0.2790908
##  [182,] 2.28317757 0.4141570
##  [183,] 0.65446853 0.6759268
##  [184,] 0.38020715 0.5792685
##  [185,] 0.64095295 0.6499631
##  [186,] 0.49271743 1.8565342
##  [187,] 0.77910785 0.8731988
##  [188,] 0.83747146 0.7693815
##  [189,] 1.12558408 1.3504568
##  [190,] 0.75855514 0.4936042
##  [191,] 0.58779736 1.5444944
##  [192,] 0.26087108 0.3362713
##  [193,] 0.31514572 1.5809000
##  [194,] 0.51316733 0.9176857
##  [195,] 0.85635080 1.2309059
##  [196,] 0.86452046 1.0329135
##  [197,] 0.24518612 0.9236570
##  [198,] 0.38742276 0.4362730
##  [199,] 0.48637309 0.4676615
##  [200,] 1.61585913 0.2446028
##  [201,] 0.98249107 1.1390330
##  [202,] 2.42523426 0.9298609
##  [203,] 6.14555228 0.8498162
##  [204,] 0.81496314 0.8541386
##  [205,] 2.48098377 0.6251541
##  [206,] 0.73432220 0.8373400
##  [207,] 2.49613298 1.1939473
##  [208,] 1.55586763 1.9311412
##  [209,] 0.42389535 0.4593381
##  [210,] 1.57096924 0.5900488
##  [211,] 0.48581526 0.5820434
##  [212,] 0.45891252 1.0332163
##  [213,] 0.53070079 0.6347142
##  [214,] 1.03285791 0.4060392
##  [215,] 0.59285005 1.4950715
##  [216,] 0.63766641 1.3210361
##  [217,] 1.38490913 0.7675485
##  [218,] 0.85329229 0.6547383
##  [219,] 0.78388539 2.9530717
##  [220,] 0.57723557 2.5224832
##  [221,] 1.38384370 0.4099661
##  [222,] 0.72026772 1.1778436
##  [223,] 2.58495614 1.8628009
##  [224,] 0.75769426 0.1209135
##  [225,] 2.88004082 0.4950732
##  [226,] 0.62381370 1.1619368
##  [227,] 1.27591113 0.7743919
##  [228,] 0.77436578 0.5540036
##  [229,] 0.64836843 0.4348550
##  [230,] 2.86708410 0.4721096
##  [231,] 0.72771994 0.5395675
##  [232,] 1.21715326 0.5633894
##  [233,] 0.64522328 1.6523152
##  [234,] 2.40036440 0.5629057
##  [235,] 1.31450784 1.2980511
##  [236,] 0.99530865 2.1033760
##  [237,] 0.46894461 1.6877363
##  [238,] 1.02083797 0.3706337
##  [239,] 0.72608744 1.8055990
##  [240,] 0.55130069 2.6027740
##  [241,] 1.21558632 1.2862675
##  [242,] 1.06092660 1.2854223
##  [243,] 0.65175482 0.3126665
##  [244,] 1.87085335 1.2987005
##  [245,] 1.45845717 1.0893296
##  [246,] 0.82850901 1.0900821
##  [247,] 1.69807666 0.5855777
##  [248,] 0.50115846 0.5860419
##  [249,] 0.37988512 0.2564932
##  [250,] 0.24827870 0.6375117
##  [251,] 0.71041217 0.6575757
##  [252,] 2.77467809 1.9539217
##  [253,] 0.31093790 0.4382178
##  [254,] 0.21981510 0.4436431
##  [255,] 0.68095528 0.1798809
##  [256,] 0.28334210 0.4197240
##  [257,] 2.70308919 0.6616115
##  [258,] 1.35689768 0.5473353
##  [259,] 0.33592741 0.3913915
##  [260,] 0.95296353 0.2662496
##  [261,] 0.34246731 0.6403873
##  [262,] 0.73357220 0.8697231
##  [263,] 0.17160922 1.0401431
##  [264,] 0.48430334 0.6859146
##  [265,] 2.94078338 1.2480282
##  [266,] 1.11909650 0.7018188
##  [267,] 2.19129654 0.5895686
##  [268,] 2.02056649 2.3494280
##  [269,] 0.28285179 1.8155906
##  [270,] 1.39478023 0.5453081
##  [271,] 0.66537993 1.6626929
##  [272,] 0.97042478 0.4391939
##  [273,] 0.86535096 1.7136298
##  [274,] 0.49524499 0.5203986
##  [275,] 0.55434563 0.6634912
##  [276,] 0.86214308 0.5487730
##  [277,] 0.38749681 1.5833841
##  [278,] 1.16782531 1.1134716
##  [279,] 0.19641024 0.6250474
##  [280,] 1.91729407 0.3817745
##  [281,] 0.29835753 0.5453640
##  [282,] 0.19356597 0.9795233
##  [283,] 0.97585643 1.7018023
##  [284,] 0.74421578 0.7787440
##  [285,] 0.37472273 1.1295818
##  [286,] 0.71644780 0.4892887
##  [287,] 0.64277807 1.1326538
##  [288,] 2.12578578 0.7277810
##  [289,] 1.37313252 1.1720864
##  [290,] 1.15063406 0.8939661
##  [291,] 1.02383412 0.4539131
##  [292,] 0.55427452 1.0089597
##  [293,] 1.06267641 0.5274970
##  [294,] 2.33146392 0.6528011
##  [295,] 0.79759196 1.5945250
##  [296,] 1.58690003 0.8760190
##  [297,] 1.62341501 0.3643366
##  [298,] 0.99474886 0.5338110
##  [299,] 0.75525075 0.6704798
##  [300,] 0.75788697 1.1325086
##  [301,] 0.20132040 1.2756771
##  [302,] 0.79772917 0.8896438
##  [303,] 0.43816627 0.5990041
##  [304,] 2.33702015 1.7893660
##  [305,] 0.53310673 0.7122714
##  [306,] 1.18597154 1.0980815
##  [307,] 1.52876800 1.2609650
##  [308,] 2.42006241 0.5929098
##  [309,] 0.87414305 1.4138840
##  [310,] 1.44693686 0.8225464
##  [311,] 1.69099505 1.0916780
##  [312,] 0.98297953 0.9864451
##  [313,] 0.97115811 1.5233027
##  [314,] 0.95008672 2.0601518
##  [315,] 0.57176457 0.4626620
##  [316,] 1.50694169 1.4665090
##  [317,] 0.83971403 0.5182919
##  [318,] 1.17850312 0.7702043
##  [319,] 0.70458528 0.1972978
##  [320,] 1.53767175 1.0355808
##  [321,] 0.34640571 0.2403992
##  [322,] 1.52101337 1.3992921
##  [323,] 0.41328799 0.6386274
##  [324,] 0.57195263 0.9262304
##  [325,] 0.63532867 0.9801472
##  [326,] 1.86592464 1.1233901
##  [327,] 0.38663095 0.5293192
##  [328,] 0.62644216 1.0785526
##  [329,] 0.92300261 0.2881430
##  [330,] 1.44096734 0.6412075
##  [331,] 1.52389456 0.3988548
##  [332,] 0.65071782 1.1126451
##  [333,] 1.63227796 1.2864149
##  [334,] 2.15250562 1.5306669
##  [335,] 0.38433051 1.4704882
##  [336,] 1.02811239 0.8212230
##  [337,] 1.27407331 1.2745042
##  [338,] 0.46807397 1.0520989
##  [339,] 0.42216101 0.4120066
##  [340,] 0.31510651 1.5409714
##  [341,] 2.11580481 0.4152330
##  [342,] 0.58521962 1.0244526
##  [343,] 0.71259032 1.8867516
##  [344,] 2.08214643 1.0787021
##  [345,] 0.68124158 0.5080444
##  [346,] 0.40314043 1.0784835
##  [347,] 1.14306532 1.0255372
##  [348,] 0.98723259 3.5493132
##  [349,] 0.61062486 1.2952428
##  [350,] 0.64803167 0.7710890
##  [351,] 0.74357466 0.3642529
##  [352,] 0.96545816 1.0849034
##  [353,] 1.51338067 0.3473848
##  [354,] 0.82846096 0.7471863
##  [355,] 1.29805256 1.2700644
##  [356,] 1.67752464 1.4977995
##  [357,] 0.75738322 0.7143068
##  [358,] 0.67360752 0.9446915
##  [359,] 1.19078040 0.6775727
##  [360,] 0.86535842 0.9080627
##  [361,] 0.79825674 0.4006153
##  [362,] 1.14471066 0.4389606
##  [363,] 0.36291405 1.5213920
##  [364,] 0.56181788 0.4813625
##  [365,] 0.56990115 0.7583227
##  [366,] 0.43723564 1.4059279
##  [367,] 0.82916737 0.6126105
##  [368,] 1.28242352 1.3170150
##  [369,] 0.97997834 0.7493162
##  [370,] 1.16696249 5.6589699
##  [371,] 0.76921378 0.8166533
##  [372,] 0.70468829 1.1900565
##  [373,] 1.79138792 2.0128180
##  [374,] 0.64453912 1.0610845
##  [375,] 0.92357996 0.9982071
##  [376,] 0.85222249 1.1902173
##  [377,] 0.38075899 2.0068498
##  [378,] 0.83342019 0.8173466
##  [379,] 1.19059721 0.6802276
##  [380,] 0.34480288 1.3710665
##  [381,] 0.37022281 0.8947034
##  [382,] 1.12551609 0.8534363
##  [383,] 0.71460756 4.0283747
##  [384,] 1.98330514 0.7579659
##  [385,] 1.28700521 0.2758770
##  [386,] 0.96022040 0.6752242
##  [387,] 0.62220452 1.2409625
##  [388,] 1.33141063 1.0956251
##  [389,] 0.83737378 1.0342799
##  [390,] 0.72151935 0.5969956
##  [391,] 0.72975937 0.6994515
##  [392,] 0.99597876 1.1109323
##  [393,] 1.10022626 0.6698022
##  [394,] 0.69207654 1.0170631
##  [395,] 1.35888650 0.4006011
##  [396,] 0.75301574 0.7120450
##  [397,] 0.49158371 1.1175651
##  [398,] 1.00195688 0.1015228
##  [399,] 2.29629964 1.3777328
##  [400,] 0.32272010 0.7290804
##  [401,] 0.17953096 1.9704037
##  [402,] 0.38904868 1.1793929
##  [403,] 0.81648157 0.8757606
##  [404,] 1.73168768 0.3897616
##  [405,] 1.48266593 0.8704389
##  [406,] 0.80930778 2.0699907
##  [407,] 1.70712347 0.3857210
##  [408,] 1.79691891 2.8166007
##  [409,] 0.65831584 1.0426219
##  [410,] 0.62845267 0.1662529
##  [411,] 1.04780847 0.7397657
##  [412,] 0.46000038 0.5390988
##  [413,] 0.55222585 0.4916957
##  [414,] 0.53078523 1.2708212
##  [415,] 0.08089913 0.6944434
##  [416,] 3.27694771 0.5489731
##  [417,] 0.81866053 1.5146732
##  [418,] 0.72366259 1.0228785
##  [419,] 1.05278702 0.5721290
##  [420,] 0.53843199 0.7268367
##  [421,] 0.65628768 0.3980721
##  [422,] 1.12168545 0.8888951
##  [423,] 1.51289609 1.1499097
##  [424,] 0.83035598 1.3534165
##  [425,] 0.60340296 1.8219189
##  [426,] 0.81102136 1.4958243
##  [427,] 0.66784452 0.9169890
##  [428,] 1.22490347 2.1789131
##  [429,] 0.27808220 1.2220281
##  [430,] 0.81306573 0.6669181
##  [431,] 0.33490273 0.4533955
##  [432,] 0.95343868 0.7793427
##  [433,] 0.62796349 1.5548269
##  [434,] 1.07802876 1.3347549
##  [435,] 0.50807284 0.3943221
##  [436,] 1.04976562 0.4165601
##  [437,] 0.47352332 0.5450595
##  [438,] 0.42097157 2.1847767
##  [439,] 0.99237602 2.0487011
##  [440,] 2.06166992 0.5603587
##  [441,] 2.46713606 0.7628723
##  [442,] 1.23458204 1.2897111
##  [443,] 0.78259567 0.3384434
##  [444,] 1.68714295 1.4217455
##  [445,] 1.00174589 0.7956361
##  [446,] 0.45437809 1.5111017
##  [447,] 0.54930544 0.6470883
##  [448,] 1.11404431 0.5382032
##  [449,] 1.96563511 0.2106650
##  [450,] 0.84921752 2.2568376
##  [451,] 0.81366904 1.6260555
##  [452,] 0.35400419 3.0595643
##  [453,] 0.54803405 1.0607976
##  [454,] 0.86086667 1.0482858
##  [455,] 0.59174002 1.1756202
##  [456,] 1.71336166 1.0245616
##  [457,] 3.47291037 1.5119148
##  [458,] 0.72213639 0.7497430
##  [459,] 0.63911633 0.9097839
##  [460,] 0.96623317 2.8812669
##  [461,] 1.27577835 1.1851489
##  [462,] 0.72136566 0.7898367
##  [463,] 0.54700216 0.3417409
##  [464,] 1.61860788 0.2179308
##  [465,] 0.59815763 1.2942483
##  [466,] 0.33399623 0.3183231
##  [467,] 2.59832776 1.3691505
##  [468,] 1.65980148 0.4460687
##  [469,] 1.69302941 0.4433658
##  [470,] 0.78030654 0.6544134
##  [471,] 0.23955609 0.5337977
##  [472,] 2.09202833 1.8130130
##  [473,] 1.72310758 2.4553426
##  [474,] 0.47112435 0.1840907
##  [475,] 0.76275168 0.3959936
##  [476,] 0.57666656 0.9160500
##  [477,] 0.32384817 0.5758912
##  [478,] 0.43227066 0.6666277
##  [479,] 1.21474194 0.3407410
##  [480,] 2.04469302 0.9644066
##  [481,] 1.39731017 0.9533926
##  [482,] 0.45095513 0.6275487
##  [483,] 0.22663088 1.4958555
##  [484,] 1.59812347 0.6001751
##  [485,] 0.73238608 1.3611388
##  [486,] 2.03226541 2.5844695
##  [487,] 0.82016054 0.6962277
##  [488,] 0.97439421 0.5559541
##  [489,] 1.01781015 1.7316152
##  [490,] 2.10958922 1.0302925
##  [491,] 1.60651943 0.3614439
##  [492,] 0.99119957 0.9007120
##  [493,] 0.99077276 0.7117627
##  [494,] 3.30039295 0.8757868
##  [495,] 1.21721154 0.7117148
##  [496,] 1.75018606 0.5139769
##  [497,] 1.12787909 0.9845356
##  [498,] 0.14669185 0.9364738
##  [499,] 1.71533073 0.1534456
##  [500,] 3.69272492 0.2462970
##  [501,] 1.08697995 3.0167614
##  [502,] 0.55389229 0.8575570
##  [503,] 1.34308433 0.2303450
##  [504,] 0.58315922 0.8442700
##  [505,] 1.09973233 2.5966845
##  [506,] 1.11556930 0.2889067
##  [507,] 0.66007020 1.0925881
##  [508,] 0.38238326 0.5971220
##  [509,] 1.15017786 1.3632907
##  [510,] 0.83595597 0.9904297
##  [511,] 0.42585403 0.8500826
##  [512,] 0.60470785 0.6565290
##  [513,] 0.74731283 1.7062220
##  [514,] 0.68054212 1.6357412
##  [515,] 1.41613818 1.2556475
##  [516,] 1.52312787 1.9097085
##  [517,] 0.56707080 0.2033227
##  [518,] 1.37261207 0.9144067
##  [519,] 0.97302203 0.4080392
##  [520,] 1.31259185 1.0256909
##  [521,] 0.58146991 1.0621807
##  [522,] 0.76125397 0.5093119
##  [523,] 0.68697169 0.2347925
##  [524,] 1.78486712 0.1888509
##  [525,] 1.05382574 0.2334618
##  [526,] 1.08427093 0.6115727
##  [527,] 1.05029012 0.4617349
##  [528,] 1.42977697 0.2719962
##  [529,] 1.87001109 0.6798916
##  [530,] 0.73252199 1.2666246
##  [531,] 1.37756729 0.6163239
##  [532,] 1.36585125 0.7784825
##  [533,] 0.31703897 2.1755728
##  [534,] 0.62102457 0.4823674
##  [535,] 0.65290608 1.4935561
##  [536,] 2.77002166 2.3648075
##  [537,] 1.08097884 0.4586641
##  [538,] 1.09748577 0.9107791
##  [539,] 0.48377379 0.9690688
##  [540,] 0.57043396 0.5709835
##  [541,] 1.85646047 1.9866537
##  [542,] 0.54657993 0.4595772
##  [543,] 0.50716002 2.2703757
##  [544,] 1.39018212 0.7555393
##  [545,] 0.87054530 0.6372790
##  [546,] 0.66065587 0.5236003
##  [547,] 1.29192708 0.4713085
##  [548,] 1.04600301 0.7069883
##  [549,] 0.81716267 0.6000037
##  [550,] 0.17468902 0.6224047
##  [551,] 0.59258379 0.7208226
##  [552,] 0.89283619 0.6469507
##  [553,] 1.24231434 1.0312131
##  [554,] 0.88263319 2.2002747
##  [555,] 1.26173443 0.7407020
##  [556,] 0.61698722 1.2372282
##  [557,] 0.87082505 2.9967348
##  [558,] 0.69817054 2.1533453
##  [559,] 1.09875550 0.8918105
##  [560,] 1.09630818 0.2263179
##  [561,] 1.13333734 1.0778343
##  [562,] 1.19809646 0.7478371
##  [563,] 3.18677298 0.6419978
##  [564,] 0.52567611 0.5852555
##  [565,] 2.92760609 0.9706206
##  [566,] 1.38528301 1.7687685
##  [567,] 0.63453709 1.5096771
##  [568,] 0.44827980 0.6260115
##  [569,] 2.53541760 0.4186630
##  [570,] 0.43746119 0.5714581
##  [571,] 1.95954664 0.6649248
##  [572,] 1.87730294 1.0751614
##  [573,] 2.20711532 1.2925296
##  [574,] 1.43316903 0.8588184
##  [575,] 0.81192677 1.2305056
##  [576,] 0.94472861 0.8771534
##  [577,] 0.42660585 0.5287869
##  [578,] 0.62716418 1.4296896
##  [579,] 0.93194116 0.5808628
##  [580,] 2.00369086 0.7943952
##  [581,] 1.01992809 1.0044794
##  [582,] 0.49053490 0.9962780
##  [583,] 0.72033174 0.5823973
##  [584,] 0.71609681 0.5199788
##  [585,] 2.76637242 1.0422581
##  [586,] 0.43797151 0.8371441
##  [587,] 0.70504001 1.7573503
##  [588,] 0.69701489 1.7312972
##  [589,] 1.91547699 1.0499265
##  [590,] 1.43663602 0.5929155
##  [591,] 0.35853381 0.7335821
##  [592,] 0.87610947 1.1499394
##  [593,] 0.98576201 0.8238989
##  [594,] 0.51266254 0.7846399
##  [595,] 1.84886348 1.8634414
##  [596,] 0.12956769 0.9568176
##  [597,] 0.45173343 0.9294444
##  [598,] 0.62575399 1.3685713
##  [599,] 0.54527364 0.7685571
##  [600,] 0.43631019 0.4984692
##  [601,] 1.80074463 0.3480476
##  [602,] 0.71670712 2.6768234
##  [603,] 0.78690373 0.5033236
##  [604,] 1.09533740 0.8776433
##  [605,] 0.61127390 1.2531063
##  [606,] 1.25620187 1.5178498
##  [607,] 1.73657118 0.4053408
##  [608,] 1.17211270 0.5330481
##  [609,] 0.67979871 2.0439311
##  [610,] 3.91142465 0.7138051
##  [611,] 1.07249261 0.7841150
##  [612,] 1.01791795 1.2237104
##  [613,] 1.92649569 0.9930600
##  [614,] 1.03270846 0.7044815
##  [615,] 0.29162684 2.1833821
##  [616,] 2.16863451 0.2143884
##  [617,] 1.96349068 1.4132107
##  [618,] 0.81074695 0.5727137
##  [619,] 0.79058724 0.8805230
##  [620,] 0.33130476 0.6212667
##  [621,] 2.27069021 1.1203673
##  [622,] 0.40567475 0.3666869
##  [623,] 0.45596696 0.9339854
##  [624,] 0.88248195 0.5978097
##  [625,] 1.25001002 2.0718046
##  [626,] 1.26221845 3.0229567
##  [627,] 0.53441091 2.2058313
##  [628,] 0.39944445 0.7851544
##  [629,] 2.45129576 0.2037824
##  [630,] 0.43634419 0.6977097
##  [631,] 0.70248138 0.6441429
##  [632,] 1.19841839 0.8238524
##  [633,] 0.42638481 1.1484546
##  [634,] 1.24752097 0.8574953
##  [635,] 0.68025214 0.4388239
##  [636,] 0.30361330 0.9438826
##  [637,] 0.65519324 1.7555445
##  [638,] 0.51202536 2.1076696
##  [639,] 1.09671517 0.3237157
##  [640,] 0.06410089 0.6456168
##  [641,] 1.28770211 0.9187439
##  [642,] 0.45237349 1.0367428
##  [643,] 1.51736784 1.2319641
##  [644,] 1.47723830 1.5225434
##  [645,] 0.74414493 0.7286364
##  [646,] 0.54730097 1.2567242
##  [647,] 0.50390316 1.4984220
##  [648,] 0.59912692 1.3006924
##  [649,] 0.91776438 3.1578943
##  [650,] 1.13050017 2.4835308
##  [651,] 1.36690891 0.8275713
##  [652,] 0.28769551 0.4335568
##  [653,] 1.45309772 1.1664955
##  [654,] 0.48393192 0.1949628
##  [655,] 2.32019470 1.8347398
##  [656,] 2.91215789 2.4479782
##  [657,] 0.64257869 1.1152602
##  [658,] 0.61146883 0.8697240
##  [659,] 0.54745071 0.8294598
##  [660,] 0.45358316 0.9094684
##  [661,] 1.86209223 1.0378279
##  [662,] 0.31280313 0.6819495
##  [663,] 0.99062940 0.5688196
##  [664,] 0.58362834 1.4014366
##  [665,] 1.29767475 0.8131148
##  [666,] 0.56368661 0.3386420
##  [667,] 1.06385174 1.1657908
##  [668,] 1.80809713 0.3119592
##  [669,] 0.34026775 0.1910332
##  [670,] 0.44402039 1.5982165
##  [671,] 1.63011924 1.8896159
##  [672,] 1.12712054 2.8358096
##  [673,] 0.68802672 1.7968997
##  [674,] 0.84164999 1.2089139
##  [675,] 0.17732828 0.6986172
##  [676,] 0.81378405 0.3538056
##  [677,] 0.24737165 0.8811370
##  [678,] 1.24936104 0.2004780
##  [679,] 1.64684394 0.4338029
##  [680,] 0.37083646 0.4663276
##  [681,] 0.87877279 0.6704607
##  [682,] 0.84518757 0.4822439
##  [683,] 0.60785272 1.8800410
##  [684,] 1.15912474 1.5032783
##  [685,] 1.14708780 0.9060396
##  [686,] 2.43032897 1.8205028
##  [687,] 0.62729858 0.5557188
##  [688,] 1.17925745 0.2147091
##  [689,] 0.43596949 0.4780095
##  [690,] 1.33615549 0.7528482
##  [691,] 1.00047034 1.3301800
##  [692,] 0.65669788 0.5665422
##  [693,] 0.47187980 0.3592098
##  [694,] 3.00277785 0.4115450
##  [695,] 1.28054376 1.3477040
##  [696,] 0.23336416 0.6106067
##  [697,] 0.87435723 0.5336155
##  [698,] 0.86162839 0.7488722
##  [699,] 1.68367652 1.2946814
##  [700,] 0.17237899 1.0905128
##  [701,] 0.74348733 0.5607427
##  [702,] 1.40771273 1.0288495
##  [703,] 0.79566112 0.4352144
##  [704,] 0.92249555 0.4774840
##  [705,] 1.08501238 1.5757038
##  [706,] 0.75082305 3.5686703
##  [707,] 0.57731478 0.4023920
##  [708,] 0.94643188 0.5548729
##  [709,] 1.33908230 1.4563204
##  [710,] 1.11852426 0.9638878
##  [711,] 1.51690307 2.2711093
##  [712,] 0.96896613 1.0824847
##  [713,] 1.90003341 0.3577276
##  [714,] 1.34869392 1.3300138
##  [715,] 1.75116271 2.1429531
##  [716,] 1.10192045 0.7127888
##  [717,] 1.92411804 1.1626389
##  [718,] 0.95782382 1.3195086
##  [719,] 0.46452066 1.0266345
##  [720,] 2.33327670 0.7535329
##  [721,] 0.70431961 0.4095754
##  [722,] 0.61889482 0.5968625
##  [723,] 2.54611203 1.1959639
##  [724,] 0.69575971 0.5184066
##  [725,] 0.55646625 0.7512479
##  [726,] 0.63142744 1.2323042
##  [727,] 1.31411681 0.4014105
##  [728,] 1.46836050 3.7874602
##  [729,] 3.87787566 0.2946527
##  [730,] 2.07044077 0.2412865
##  [731,] 0.31472390 0.8525800
##  [732,] 1.19488888 3.6282645
##  [733,] 0.77512006 1.0336877
##  [734,] 1.54727412 1.1484797
##  [735,] 0.76731719 4.8371090
##  [736,] 0.90143834 0.8057870
##  [737,] 0.60571514 0.5362546
##  [738,] 0.66206677 0.9580251
##  [739,] 1.25268583 0.7769230
##  [740,] 0.69707197 0.4235283
##  [741,] 1.90191685 0.2133243
##  [742,] 0.75484078 1.5204905
##  [743,] 0.86950891 0.6666057
##  [744,] 1.25934808 0.9322101
##  [745,] 2.20075944 1.5095102
##  [746,] 1.61487831 0.4112771
##  [747,] 0.72795970 0.7164128
##  [748,] 2.16613421 0.9706847
##  [749,] 0.42425513 0.9650048
##  [750,] 0.82657824 0.5628484
##  [751,] 1.25700522 0.8411519
##  [752,] 0.58075094 0.7177401
##  [753,] 1.56374970 0.8098214
##  [754,] 0.12293582 1.6659026
##  [755,] 0.72355632 0.6119912
##  [756,] 0.64205408 0.5095687
##  [757,] 1.91215703 0.6281910
##  [758,] 2.36627290 1.1427033
##  [759,] 0.29231523 1.0357593
##  [760,] 1.93931725 1.1536789
##  [761,] 1.34474488 1.1324691
##  [762,] 0.40912285 0.2581767
##  [763,] 0.58505554 0.6586129
##  [764,] 0.53584804 0.2087720
##  [765,] 0.49260728 0.3984549
##  [766,] 0.86919870 0.5143073
##  [767,] 0.61580808 0.5680969
##  [768,] 2.15011608 0.6116784
##  [769,] 0.33507967 0.5167507
##  [770,] 0.48098977 0.4555907
##  [771,] 0.29323257 0.3241618
##  [772,] 0.43807658 0.5522117
##  [773,] 0.59945977 1.2956945
##  [774,] 0.64754053 0.8014917
##  [775,] 4.30185599 0.3846354
##  [776,] 0.43788330 1.0821829
##  [777,] 0.22300111 0.6332761
##  [778,] 0.22293963 0.8051476
##  [779,] 0.58513127 0.8011112
##  [780,] 1.59291443 0.5808460
##  [781,] 0.58392704 1.1765356
##  [782,] 0.17677251 0.8250374
##  [783,] 0.56452957 2.1078476
##  [784,] 0.30953936 1.1589478
##  [785,] 0.99090843 1.1517757
##  [786,] 0.98040959 2.0987666
##  [787,] 0.23938546 0.9690597
##  [788,] 0.77750479 0.8158301
##  [789,] 0.37100569 1.0120035
##  [790,] 3.01703720 0.7501027
##  [791,] 1.06976902 1.7932978
##  [792,] 1.30279937 0.7004976
##  [793,] 1.59848449 0.7508667
##  [794,] 0.80944427 0.2542158
##  [795,] 0.88999117 0.9420539
##  [796,] 0.52528486 0.5221366
##  [797,] 0.50007397 0.3643280
##  [798,] 0.82042810 0.6728878
##  [799,] 0.78454906 1.1416047
##  [800,] 0.58017452 0.8771815
##  [801,] 0.58733497 1.9087884
##  [802,] 0.62780148 0.8366287
##  [803,] 0.53714647 0.4296790
##  [804,] 1.06521888 1.0714702
##  [805,] 1.85018006 1.0976054
##  [806,] 0.47571805 1.0192515
##  [807,] 0.84355869 0.2971449
##  [808,] 0.83129419 1.2064371
##  [809,] 2.18007347 1.1860421
##  [810,] 0.62921261 1.7230174
##  [811,] 0.89046462 1.1517023
##  [812,] 0.79155056 0.7318432
##  [813,] 2.97201729 1.2631694
##  [814,] 0.40443798 2.2559094
##  [815,] 2.16070158 1.0180115
##  [816,] 2.20259456 1.1656339
##  [817,] 0.98586925 0.6101497
##  [818,] 1.01084350 1.0439240
##  [819,] 0.99421000 0.8471173
##  [820,] 1.42289198 1.5079117
##  [821,] 1.77763822 0.2671525
##  [822,] 0.72740517 0.9221009
##  [823,] 1.34807090 0.8437444
##  [824,] 1.97368030 0.4628382
##  [825,] 1.87347928 0.1084476
##  [826,] 0.77189029 0.6582549
##  [827,] 0.51157048 0.8574418
##  [828,] 0.61625527 0.4604354
##  [829,] 0.33021038 1.2718445
##  [830,] 1.05619982 0.9252157
##  [831,] 0.41004443 0.2056754
##  [832,] 0.43423724 0.3809702
##  [833,] 0.34953049 0.9424481
##  [834,] 0.52316591 1.0068131
##  [835,] 0.48179467 0.3467477
##  [836,] 0.72180482 0.9515771
##  [837,] 0.51713112 0.6882767
##  [838,] 1.04191204 0.5468752
##  [839,] 1.72526810 0.6775245
##  [840,] 1.35627011 0.1454602
##  [841,] 0.98480166 0.9372470
##  [842,] 1.07184983 0.3412724
##  [843,] 0.71307595 1.4812772
##  [844,] 1.73582392 1.0994799
##  [845,] 0.81931911 0.5207496
##  [846,] 0.68225064 1.1549170
##  [847,] 2.39552530 0.3271115
##  [848,] 1.14276671 0.5780169
##  [849,] 0.15456494 0.4457840
##  [850,] 0.72135799 0.8564635
##  [851,] 1.27978110 0.9607251
##  [852,] 0.75500830 0.7582362
##  [853,] 1.12691139 0.4718323
##  [854,] 0.66224421 1.3208272
##  [855,] 1.15967096 0.8357068
##  [856,] 1.29891517 1.1874824
##  [857,] 0.70987749 0.6776459
##  [858,] 0.72410982 0.7047933
##  [859,] 0.59130087 1.1651493
##  [860,] 0.79299956 0.4904724
##  [861,] 0.43132159 0.4320980
##  [862,] 0.21940303 0.9033722
##  [863,] 3.64712935 1.3515488
##  [864,] 1.20192997 0.2259046
##  [865,] 0.82761195 1.5669602
##  [866,] 0.64242190 0.3229068
##  [867,] 1.04300250 1.0263216
##  [868,] 0.76909200 0.7987564
##  [869,] 0.47430078 0.8824073
##  [870,] 1.20097867 0.9436169
##  [871,] 0.75480514 0.8015523
##  [872,] 2.07067423 0.5653110
##  [873,] 1.01091984 1.0749982
##  [874,] 0.74822830 0.2524207
##  [875,] 0.50627477 1.4498846
##  [876,] 3.67380311 0.3947072
##  [877,] 0.61558279 0.4239835
##  [878,] 2.02852571 0.4716817
##  [879,] 3.00453875 0.5999413
##  [880,] 0.47233078 0.9914937
##  [881,] 1.19010198 1.8271512
##  [882,] 0.84069588 0.5769016
##  [883,] 1.21017329 1.9544810
##  [884,] 1.45286238 2.6720118
##  [885,] 0.50628462 0.4703280
##  [886,] 3.33880529 0.6767213
##  [887,] 0.42762127 0.7027358
##  [888,] 0.54730047 1.0566285
##  [889,] 1.28496402 1.6346632
##  [890,] 2.59480104 0.5306793
##  [891,] 0.87756229 3.0428468
##  [892,] 1.91582978 0.5551037
##  [893,] 0.40764128 1.1613811
##  [894,] 0.46184048 0.4867443
##  [895,] 0.51248911 0.5677282
##  [896,] 0.44506752 0.4898592
##  [897,] 2.84762507 2.1723468
##  [898,] 0.66643576 0.6528615
##  [899,] 0.62206285 0.3031422
##  [900,] 1.21292638 0.5493861
##  [901,] 0.99578030 0.6886083
##  [902,] 0.76168383 0.4104400
##  [903,] 1.93261775 0.4114208
##  [904,] 0.69262381 0.4837668
##  [905,] 0.43014873 0.8563557
##  [906,] 0.66553174 0.6027835
##  [907,] 1.41819254 0.3048096
##  [908,] 0.79747221 1.5975816
##  [909,] 1.83927012 0.8469353
##  [910,] 0.66057521 0.7649361
##  [911,] 0.81194740 0.6182865
##  [912,] 0.67461520 1.1008965
##  [913,] 1.88797929 0.9637572
##  [914,] 0.77479496 0.8749445
##  [915,] 0.68644519 0.9762334
##  [916,] 0.86744229 0.8108681
##  [917,] 0.93694062 0.6633495
##  [918,] 0.63062012 1.2593403
##  [919,] 1.26478804 2.0390032
##  [920,] 1.25157351 0.5196868
##  [921,] 0.35284311 0.4603766
##  [922,] 0.78456296 0.4206922
##  [923,] 0.32472507 0.4323342
##  [924,] 0.25727667 1.6986068
##  [925,] 1.70763546 2.0250919
##  [926,] 0.67218346 0.9214438
##  [927,] 0.70707210 0.5233351
##  [928,] 0.50835619 0.5687120
##  [929,] 0.86851381 0.8228832
##  [930,] 2.10031865 0.6848575
##  [931,] 0.26361145 0.6546123
##  [932,] 0.98148208 0.2832739
##  [933,] 2.39284956 1.4650066
##  [934,] 0.04023172 1.0594909
##  [935,] 0.95190734 1.9269836
##  [936,] 0.45612927 0.7400490
##  [937,] 0.39002183 0.4475721
##  [938,] 0.70261876 1.6190117
##  [939,] 0.77694349 1.1815229
##  [940,] 0.92109829 0.5445483
##  [941,] 0.84989611 0.7826478
##  [942,] 0.77277748 0.6856594
##  [943,] 0.62829219 0.7889788
##  [944,] 0.43509812 0.9087642
##  [945,] 1.14501891 1.5746326
##  [946,] 0.76629638 0.6343912
##  [947,] 0.93991113 0.1595323
##  [948,] 0.93773796 1.6091783
##  [949,] 0.87200184 1.1630009
##  [950,] 1.33776264 1.1057329
##  [951,] 0.73711583 0.8860939
##  [952,] 0.44261755 0.9148329
##  [953,] 0.71741567 1.3538343
##  [954,] 0.21711328 0.4746137
##  [955,] 0.82589914 1.4652563
##  [956,] 0.61546484 0.7581856
##  [957,] 2.55694374 0.9901904
##  [958,] 0.48593468 1.6158228
##  [959,] 0.29848584 1.1080153
##  [960,] 0.39015486 0.9626104
##  [961,] 0.29414862 0.5014723
##  [962,] 0.55295930 1.2537841
##  [963,] 1.76909985 0.8207305
##  [964,] 0.70067870 2.2439625
##  [965,] 2.97164954 0.6126692
##  [966,] 0.34168400 0.8891289
##  [967,] 0.56317001 0.7408331
##  [968,] 2.15714643 1.1597120
##  [969,] 0.65554662 0.8532540
##  [970,] 1.29204481 0.2259971
##  [971,] 0.48160159 0.6233436
##  [972,] 1.57116442 1.5033480
##  [973,] 1.02378447 0.7649779
##  [974,] 0.44172995 2.7319639
##  [975,] 0.69508738 0.8874048
##  [976,] 0.53530281 1.0046028
##  [977,] 0.86731373 0.8765445
##  [978,] 0.55448186 0.3190288
##  [979,] 0.91530582 0.4833298
##  [980,] 0.43981035 0.8824495
##  [981,] 1.34811785 2.8718457
##  [982,] 3.73387358 0.6677432
##  [983,] 0.83973527 0.9602228
##  [984,] 1.82446389 0.2438962
##  [985,] 1.20317198 0.6127901
##  [986,] 1.28636216 0.1840904
##  [987,] 0.31453472 0.7717767
##  [988,] 1.26768606 1.0169165
##  [989,] 2.29964320 0.6336563
##  [990,] 0.92030976 1.9316230
##  [991,] 1.11494021 0.6238103
##  [992,] 0.77004246 1.0983543
##  [993,] 1.39372409 0.7553803
##  [994,] 0.67723414 0.8657639
##  [995,] 0.34473828 0.9032336
##  [996,] 0.81423396 2.2875280
##  [997,] 0.49961996 1.1116194
##  [998,] 0.85828440 1.4167456
##  [999,] 0.57267189 0.3282864
## 
## $model.matrix
##    (Intercept) microsite1 shrub_density
## 1            1          1            11
## 2            1          1            12
## 3            1         -1             0
## 4            1         -1             0
## 5            1          1            11
## 6            1          1            10
## 7            1         -1             0
## 8            1         -1             0
## 9            1          1            14
## 10           1          1            13
## 11           1         -1             0
## 12           1         -1             0
## 13           1          1            11
## 14           1          1            11
## 15           1         -1             0
## 16           1         -1             0
## 17           1          1            10
## 18           1          1            11
## 19           1          1            11
## 20           1          1            10
## 21           1         -1             0
## 22           1         -1             0
## 23           1         -1             0
## 24           1         -1             0
## 
## $terms
## pca_data_final ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_final, microsite, shrub_density)
## attr(,"factors")
##                microsite shrub_density microsite:shrub_density
## pca_data_final         0             0                       0
## microsite              1             0                       1
## shrub_density          0             1                       1
## attr(,"term.labels")
## [1] "microsite"               "shrub_density"          
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## 
## attr(,"class")
## [1] "adonis"
dist_final <- vegdist(pca_data_final, species = "bray")
res_final <- pcoa(dist_final)
p02 <- as.data.frame(res_final$vectors)%>%
  dplyr::select(Axis.1, Axis.2) %>%
  bind_cols(env_final,.)

p02$microsite <- ifelse(p02$microsite == "Density", "Shrub", p02$microsite)

pcoa_final <- ggplot(p02, aes(Axis.1, Axis.2, group = microsite)) +
  geom_point(aes(color = microsite)) +
  geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
  scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  labs(color = "Microsite")
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
pcoa_final <- pcoa_final + labs(x = "Shrub Density Gradient", y = "Community Composition") + scale_color_manual(
    values = c("Shrub" = "#009900", "Open" = "#0066cc"), # Change color codes here
    labels = c("Shrub" = "Shrub", "Open" = "Open")) + coord_fixed(ratio = 1)
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
pcoa_final

model020 <- betadisper(dist_final, env_final$microsite)
model020
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist_final, group = env_final$microsite)
## 
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
## 
## Average distance to median:
## Density    Open 
##  0.5034  0.4680 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model020)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq   Mean Sq F value Pr(>F)
## Groups     1 0.00750 0.0074952  0.2553 0.6184
## Residuals 22 0.64591 0.0293594
permutest(model020,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
## Groups     1 0.00750 0.0074952 0.2553     99   0.68
## Residuals 22 0.64591 0.0293594                     
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##         Density Open
## Density         0.67
## Open     0.6184
model020.HSD <- TukeyHSD(model020)
model020.HSD
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = distances ~ group, data = df)
## 
## $group
##                     diff        lwr       upr     p adj
## Open-Density -0.03534393 -0.1804147 0.1097268 0.6183989
boxplot(model020)

model030 <- betadisper(dist_final, env_final$shrub_density)
model030
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist_final, group = env_final$shrub_density)
## 
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
## 
## Average distance to median:
##      0     10     11     12     13     14 
## 0.4680 0.3766 0.4847 0.0000 0.0000 0.0000 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model030)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq Mean Sq F value  Pr(>F)  
## Groups     5 0.58003 0.11601   2.233 0.09554 .
## Residuals 18 0.93511 0.05195                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model030,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq Mean Sq     F N.Perm Pr(>F)
## Groups     5 0.58003 0.11601 2.233     99   0.12
## Residuals 18 0.93511 0.05195                    
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##          0      10      11 12 13 14
## 0          0.54000 0.91000         
## 10 0.55757         0.60000         
## 11 0.86321 0.60937                 
## 12                                 
## 13                                 
## 14
model030.HSD <- TukeyHSD(model030)
boxplot(model030)

### Shows a significant difference between community composition of tested sites (Carrizo -> Cuyama -> Tecopa)
model040 <- betadisper(dist_final, env_final$site)
model040
## 
##  Homogeneity of multivariate dispersions
## 
## Call: betadisper(d = dist_final, group = env_final$site)
## 
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
## 
## Average distance to median:
## Carrizo  Cuyama  Tecopa 
##  0.2734  0.3349  0.4558 
## 
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
##  PCoA1  PCoA2  PCoA3  PCoA4  PCoA5  PCoA6  PCoA7  PCoA8 
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model040)
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq F value  Pr(>F)  
## Groups     2 0.13782 0.068911  3.7363 0.04091 *
## Residuals 21 0.38732 0.018444                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model040,pairwise = TRUE, permutations = 99)
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
## 
## Response: Distances
##           Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
## Groups     2 0.13782 0.068911 3.7363     99   0.07 .
## Residuals 21 0.38732 0.018444                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
##          Carrizo   Cuyama Tecopa
## Carrizo          0.420000   0.05
## Cuyama  0.425689            0.07
## Tecopa  0.016550 0.067329
model040.HSD <- TukeyHSD(model040)
supp_plot <- boxplot(model040, xlab = "Region")

### Temperature Data 2022

Temp_2022 <- read_csv("Temp Data 2022.csv")
## Rows: 52443 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): researcher, site, site_code, microsite, time_block
## dbl  (5): microsite_number, pendent_number, pendent_ID, rep, temp
## date (1): date
## time (1): time
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Temp_2022_final <- Temp_2022 %>%
  group_by(site_code, microsite) %>%
  summarise(mean_temp = mean(temp), max_temp = max(temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
###Ground Temp Data
Ground_Temp_2022 <- read_csv("Gradient Density Datasheet 2022.csv")
## Rows: 695 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (4): site, site_code, date, microsite
## dbl (22): rep, microsite_number, shrub_ID, shrub_number, total_shrub, micros...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ground_Temp_2022_final <- Ground_Temp_2022 %>%
  group_by(site_code, microsite) %>%
  summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp)) 
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
### Combine this with new_data
### Aridity Data
aridity <- read_csv("regional_sites_2022.csv")
## Rows: 51 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): state, desert, region, experiment, site_acronym, sub_site, site_co...
## dbl  (7): lat, long, elevation, MAT, MAP, aridity, area_block_m2
## num  (1): area_m2
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
aridity <- aridity %>%
 dplyr::select(site_code, aridity)
final_2022<- photo%>%
  group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
  summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.
final_2022 <- final_2022 %>%
  filter(common_name != "Blank")%>% filter(common_name != "No CV Result")

density_simple <- final_2022 %>%
  group_by(site, site_code, year, microsite, plot, shrub_density) %>%
  summarise(animals = sum(captures), richness = n()) %>%
 rename(microsite_number = 'plot')
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.
#write.csv(density_simple, file = "density_simple_fixed.csv") Output density simple because it does not include the sites with 0 observations
density_simple_fixed <- read.csv("density_simple_fixed.csv")
density_simple_fixed <- density_simple_fixed[,-1]
### This is for 2022 evenness
vegan_data <- animals_density ### Created new df for pca data
vegan_data <- vegan_data %>%
  spread(common_name, captures) %>%
  ungroup() %>%
  replace(is.na(.),0)


evenness_data <- vegan_data %>%
  group_by(site_code, microsite) %>%
  summarize(across(5:29, ~diversity(., index = "shannon")))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
evenness_data <- na.omit(evenness_data)

evenness_data <- evenness_data %>%
  mutate(Average_Evenness = rowMeans(across(5:26)))

evenness_data <- evenness_data %>%
  dplyr::select(site_code, microsite, Average_Evenness) 
### Join evenness_data with density_simple data
new_data <- inner_join(density_simple_fixed, evenness_data, by = c("site_code", "microsite"))

### Combine new data with logger temp data from 2022
new_data <- inner_join(new_data, Temp_2022_final,by = c("site_code", "microsite"))

### Need to add ground temperature from hand recordings then 2022 is ready!
new_data <- inner_join(new_data, Ground_Temp_2022_final, by = c("site_code", "microsite"))

### Combine all data with aridity data of the sites we have
new_data <- inner_join(new_data, aridity, by = c("site_code"))

### 2022 data is now cleaned and ready to go
### Clean up 2023 data so it can be properly combined with 2022 data

final_2023<- photo_2023%>%
  group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
  summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.
density_simple_2023 <- final_2023 %>%
  group_by(site, site_code, year, microsite, plot, shrub_density) %>%
  summarise(animals = sum(captures), richness = n()) %>%
 rename(microsite_number = 'plot')
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.
write.csv(density_simple_2023, file = "density_simple_fixed_2023.csv")


### This is for 2023 evenness
vegan_data_2023 <- animals_density_2023 ### Created new df for pca data
vegan_data_2023 <- vegan_data_2023 %>%
  spread(common_name, captures) %>%
  ungroup() %>%
  replace(is.na(.),0)

evenness_data_2023 <- vegan_data_2023 %>%
  group_by(site_code, microsite) %>%
  summarize(across(5:27, ~diversity(., index = "shannon")))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
evenness_data_2023 <- na.omit(evenness_data_2023)

evenness_data_2023 <- evenness_data_2023 %>%
  mutate(Average_Evenness = rowMeans(across(5:24)))
evenness_data_2023 <- evenness_data_2023 %>%
  dplyr::select(site_code, microsite, Average_Evenness) 
new_data_2023 <- inner_join(density_simple_2023, evenness_data_2023, by = c("site_code", "microsite"))

new_data_2023$site_code <- gsub("Tecopa_Shrub", "Tecopa_shrub", new_data_2023$site_code)
new_data_2023$site_code <- gsub("Tecopa_Open", "Tecopa_open", new_data_2023$site_code)

### Follow same steps as above 2022 data clean up. Start with logger temp, then ground temp, then aridity.

### Combine new_data_2023 to Temp_2023
Temp_2023 <- read_csv("Temp Data 2023.csv") %>% filter(!(temp > 50)) %>% filter(!(temp < -47))
## Rows: 24760 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (4): researcher, site, site_code, microsite
## dbl  (5): microsite_number, pendant_number, pendant_ID, rep, temp
## date (1): date
## time (1): time
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Temp_2023_final <- Temp_2023 %>%
  group_by(site_code, microsite) %>%
  summarise(mean_temp = mean(temp), max_temp = max(temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
new_data_2023 <- inner_join(new_data_2023, Temp_2023_final, by = c("site_code", "microsite"))

### Combine Ground Temp Data from 2023
Ground_Temp_2023 <- read_csv("Gradient Density Datasheet 2023.csv")
## New names:
## Rows: 587 Columns: 29
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): site, site_code, date, microsite dbl (25): rep, microsite_number,
## shrub_ID, shrub_number, total_shrub, micros...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...28`
## • `` -> `...29`
Ground_Temp_2023_final <- Ground_Temp_2023 %>%
  group_by(site_code, microsite, microsite_number) %>%
  summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp)) 
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
new_data_2023 <- inner_join(new_data_2023, Ground_Temp_2023_final, by = c("site_code", "microsite", "microsite_number"))

### Aridity data is already set up from 2022 data
new_data_2023 <- inner_join(new_data_2023, aridity, by = c("site_code"))

### 2023 Data is now cleaned and ready to be combined with 2022 data
final_data <- rbind(new_data, new_data_2023)

All Data is combined now into one clean dataframe that can be used to generate figures and statistics (Use final_data)

Stats for Shrub Density

library(ggpubr)
shapiro.test(final_data$animals)
## 
##  Shapiro-Wilk normality test
## 
## data:  final_data$animals
## W = 0.62948, p-value = 1.551e-09
ggqqplot(final_data$animals)

### Stats to show that the aridity across tested regions significantly varies. This needs to be one of the FIRST things you put in your results.
anova_result <- aov(aridity ~ site, data = final_data)
summary(anova_result)
##             Df Sum Sq Mean Sq F value Pr(>F)    
## site         2 103.52   51.76    3639 <2e-16 ***
## Residuals   43   0.61    0.01                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(anova_result, pairwise ~ site)
## $emmeans
##  site    emmean     SE df lower.CL upper.CL
##  Carrizo  3.252 0.0319 43    3.188    3.316
##  Cuyama   3.701 0.0298 43    3.641    3.762
##  Tecopa   0.365 0.0298 43    0.305    0.425
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast         estimate     SE df t.ratio p.value
##  Carrizo - Cuyama   -0.449 0.0436 43 -10.297  <.0001
##  Carrizo - Tecopa    2.887 0.0436 43  66.147  <.0001
##  Cuyama - Tecopa     3.336 0.0422 43  79.126  <.0001
## 
## P value adjustment: tukey method for comparing a family of 3 estimates
tukey_result <- TukeyHSD(anova_result)

# View the Tukey's HSD results
print(tukey_result)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = aridity ~ site, data = final_data)
## 
## $site
##                      diff        lwr        upr p adj
## Cuyama-Carrizo  0.4494073  0.3434585  0.5553561     0
## Tecopa-Carrizo -2.8870527 -2.9930014 -2.7811039     0
## Tecopa-Cuyama  -3.3364600 -3.4388162 -3.2341037     0
### Tukey test shows Cuyama less arid than Carrizo, Carrizo less arid than Tecopa, Cuyama less arid than Tecopa
### Abundance
model1 <- glm(animals~shrub_density*site*year+aridity, family = "gaussian", data = final_data)
model1
## 
## Call:  glm(formula = animals ~ shrub_density * site * year + aridity, 
##     family = "gaussian", data = final_data)
## 
## Coefficients:
##                   (Intercept)                  shrub_density  
##                     7.551e+04                      1.061e+04  
##                    siteCuyama                     siteTecopa  
##                     4.027e+05                     -9.065e+04  
##                          year                        aridity  
##                    -3.736e+01                      3.297e+01  
##      shrub_density:siteCuyama       shrub_density:siteTecopa  
##                    -1.559e+03                     -1.211e+04  
##            shrub_density:year                siteCuyama:year  
##                    -5.245e+00                     -1.991e+02  
##               siteTecopa:year  shrub_density:siteCuyama:year  
##                     4.485e+01                      7.703e-01  
## shrub_density:siteTecopa:year  
##                     5.986e+00  
## 
## Degrees of Freedom: 45 Total (i.e. Null);  33 Residual
## Null Deviance:       765800 
## Residual Deviance: 302900    AIC: 563
anova(model1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                       45     765773              
## shrub_density            1    18242        44     747531 0.1586319    
## site                     2   139832        42     607700 0.0004923 ***
## year                     1   137687        41     470013 0.0001076 ***
## aridity                  1       55        40     469958 0.9380678    
## shrub_density:site       2     3544        38     466414 0.8244495    
## shrub_density:year       1     5580        37     460833 0.4355873    
## site:year                2   155562        35     305272 0.0002090 ***
## shrub_density:site:year  2     2342        33     302929 0.8802245    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e2 <- emmeans(model1, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e2
## $emmeans
##  site    year emmean  SE df lower.CL upper.CL
##  Carrizo 2022   83.8 122 33     -164      332
##  Cuyama  2022  235.1 176 33     -123      593
##  Tecopa  2022   74.6 274 33     -482      631
##  Carrizo 2023   15.7 111 33     -210      242
##  Cuyama  2023  -27.6 176 33     -386      330
##  Tecopa  2023   86.4 274 33     -470      643
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                            estimate    SE df t.ratio p.value
##  Carrizo year2022 - Cuyama year2022   -151.28  73.5 33  -2.057  0.3336
##  Carrizo year2022 - Tecopa year2022      9.25 391.4 33   0.024  1.0000
##  Carrizo year2022 - Carrizo year2023    68.15  54.7 33   1.246  0.8109
##  Carrizo year2022 - Cuyama year2023    111.45  73.5 33   1.516  0.6570
##  Carrizo year2022 - Tecopa year2023     -2.59 391.4 33  -0.007  1.0000
##  Cuyama year2022 - Tecopa year2022     160.53 446.7 33   0.359  0.9991
##  Cuyama year2022 - Carrizo year2023    219.43  87.3 33   2.515  0.1490
##  Cuyama year2022 - Cuyama year2023     262.73  47.9 33   5.480  0.0001
##  Cuyama year2022 - Tecopa year2023     148.70 446.7 33   0.333  0.9994
##  Tecopa year2022 - Carrizo year2023     58.90 378.5 33   0.156  1.0000
##  Tecopa year2022 - Cuyama year2023     102.20 446.7 33   0.229  0.9999
##  Tecopa year2022 - Tecopa year2023     -11.83  48.2 33  -0.245  0.9999
##  Carrizo year2023 - Cuyama year2023     43.30  87.3 33   0.496  0.9960
##  Carrizo year2023 - Tecopa year2023    -70.73 378.5 33  -0.187  1.0000
##  Cuyama year2023 - Tecopa year2023    -114.04 446.7 33  -0.255  0.9998
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
shapiro.test(final_data$richness)
## 
##  Shapiro-Wilk normality test
## 
## data:  final_data$richness
## W = 0.96052, p-value = 0.1201
ggqqplot(final_data$richness)

### Richness Stats
model2 <- glm(richness~shrub_density*site*year +aridity, family = "gaussian", data = final_data)
model2
## 
## Call:  glm(formula = richness ~ shrub_density * site * year + aridity, 
##     family = "gaussian", data = final_data)
## 
## Coefficients:
##                   (Intercept)                  shrub_density  
##                    -1.049e+03                     -9.984e+01  
##                    siteCuyama                     siteTecopa  
##                     1.799e+04                     -6.515e+03  
##                          year                        aridity  
##                     5.227e-01                     -5.391e-01  
##      shrub_density:siteCuyama       shrub_density:siteTecopa  
##                    -2.708e+02                     -1.922e+02  
##            shrub_density:year                siteCuyama:year  
##                     4.939e-02                     -8.895e+00  
##               siteTecopa:year  shrub_density:siteCuyama:year  
##                     3.219e+00                      1.339e-01  
## shrub_density:siteTecopa:year  
##                     9.508e-02  
## 
## Degrees of Freedom: 45 Total (i.e. Null);  33 Residual
## Null Deviance:       575.9 
## Residual Deviance: 79.32     AIC: 183.6
anova_results2 <- aov(model2, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'test' will be disregarded
anova(model2, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: richness
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                       45     575.91              
## shrub_density            1   27.132        44     548.78 0.0007803 ***
## site                     2  164.760        42     384.02 1.305e-15 ***
## year                     1    6.218        41     377.80 0.1077656    
## aridity                  1    1.307        40     376.50 0.4609209    
## shrub_density:site       2    1.941        38     374.56 0.6678024    
## shrub_density:year       1    3.261        37     371.29 0.2441039    
## site:year                2  290.938        35      80.36 < 2.2e-16 ***
## shrub_density:site:year  2    1.034        33      79.32 0.8064261    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results2)
##                         Df Sum Sq Mean Sq F value   Pr(>F)    
## shrub_density            1  27.13   27.13  11.287  0.00198 ** 
## site                     2 164.76   82.38  34.272 8.82e-09 ***
## year                     1   6.22    6.22   2.587  0.11729    
## aridity                  1   1.31    1.31   0.544  0.46613    
## shrub_density:site       2   1.94    0.97   0.404  0.67106    
## shrub_density:year       1   3.26    3.26   1.357  0.25246    
## site:year                2 290.94  145.47  60.519 9.11e-12 ***
## shrub_density:site:year  2   1.03    0.52   0.215  0.80755    
## Residuals               33  79.32    2.40                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e3 <- emmeans(model2, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e3  ### Ok now this all works.
## $emmeans
##  site    year emmean   SE df lower.CL upper.CL
##  Carrizo 2022  6.356 1.97 33    2.346    10.37
##  Cuyama  2022 12.443 2.85 33    6.649    18.24
##  Tecopa  2022  0.208 4.43 33   -8.799     9.21
##  Carrizo 2023  7.168 1.80 33    3.513    10.82
##  Cuyama  2023  5.146 2.85 33   -0.648    10.94
##  Tecopa  2023  4.797 4.43 33   -4.209    13.80
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                            estimate    SE df t.ratio p.value
##  Carrizo year2022 - Cuyama year2022    -6.087 1.190 33  -5.116  0.0002
##  Carrizo year2022 - Tecopa year2022     6.148 6.333 33   0.971  0.9239
##  Carrizo year2022 - Carrizo year2023   -0.813 0.885 33  -0.918  0.9390
##  Carrizo year2022 - Cuyama year2023     1.210 1.190 33   1.017  0.9090
##  Carrizo year2022 - Tecopa year2023     1.558 6.333 33   0.246  0.9999
##  Cuyama year2022 - Tecopa year2022     12.235 7.229 33   1.692  0.5461
##  Cuyama year2022 - Carrizo year2023     5.274 1.412 33   3.736  0.0085
##  Cuyama year2022 - Cuyama year2023      7.297 0.776 33   9.405  <.0001
##  Cuyama year2022 - Tecopa year2023      7.645 7.229 33   1.058  0.8944
##  Tecopa year2022 - Carrizo year2023    -6.960 6.125 33  -1.136  0.8626
##  Tecopa year2022 - Cuyama year2023     -4.938 7.229 33  -0.683  0.9826
##  Tecopa year2022 - Tecopa year2023     -4.590 0.781 33  -5.880  <.0001
##  Carrizo year2023 - Cuyama year2023     2.022 1.412 33   1.432  0.7076
##  Carrizo year2023 - Tecopa year2023     2.371 6.125 33   0.387  0.9988
##  Cuyama year2023 - Tecopa year2023      0.348 7.229 33   0.048  1.0000
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
ggqqplot(final_data$Average_Evenness)

### Evenness
model3 <- glm(Average_Evenness~shrub_density*site*year + aridity, family = "gaussian", data = final_data)
model3
## 
## Call:  glm(formula = Average_Evenness ~ shrub_density * site * year + 
##     aridity, family = "gaussian", data = final_data)
## 
## Coefficients:
##                   (Intercept)                  shrub_density  
##                     1.175e+02                     -8.606e+00  
##                    siteCuyama                     siteTecopa  
##                     2.013e+02                     -3.866e+02  
##                          year                        aridity  
##                    -5.801e-02                     -2.568e-02  
##      shrub_density:siteCuyama       shrub_density:siteTecopa  
##                     1.008e+01                     -9.806e+00  
##            shrub_density:year                siteCuyama:year  
##                     4.257e-03                     -9.952e-02  
##               siteTecopa:year  shrub_density:siteCuyama:year  
##                     1.911e-01                     -4.981e-03  
## shrub_density:siteTecopa:year  
##                     4.848e-03  
## 
## Degrees of Freedom: 45 Total (i.e. Null);  33 Residual
## Null Deviance:       0.3064 
## Residual Deviance: 0.02358   AIC: -190
anova_results3 <- aov(model3, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'test' will be disregarded
anova(model3, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: Average_Evenness
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                       45   0.306353              
## shrub_density            1 0.020430        44   0.285923 8.936e-08 ***
## site                     2 0.011457        42   0.274466 0.0003297 ***
## year                     1 0.000058        41   0.274408 0.7763951    
## aridity                  1 0.000022        40   0.274386 0.8618492    
## shrub_density:site       2 0.001475        38   0.272911 0.3562435    
## shrub_density:year       1 0.000932        37   0.271980 0.2535206    
## site:year                2 0.242134        35   0.029845 < 2.2e-16 ***
## shrub_density:site:year  2 0.006265        33   0.023580 0.0124756 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results3)
##                         Df  Sum Sq Mean Sq F value   Pr(>F)    
## shrub_density            1 0.02043 0.02043  28.592 6.62e-06 ***
## site                     2 0.01146 0.00573   8.017  0.00145 ** 
## year                     1 0.00006 0.00006   0.081  0.77817    
## aridity                  1 0.00002 0.00002   0.030  0.86291    
## shrub_density:site       2 0.00148 0.00074   1.032  0.36746    
## shrub_density:year       1 0.00093 0.00093   1.304  0.26174    
## site:year                2 0.24213 0.12107 169.432  < 2e-16 ***
## shrub_density:site:year  2 0.00627 0.00313   4.384  0.02049 *  
## Residuals               33 0.02358 0.00071                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e4 <- emmeans(model3, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e4  ### Need to double check emmeans it does not match the figure
## $emmeans
##  site    year  emmean     SE df lower.CL upper.CL
##  Carrizo 2022  0.1186 0.0340 33  0.04944    0.188
##  Cuyama  2022  0.2302 0.0491 33  0.13028    0.330
##  Tecopa  2022 -0.0251 0.0763 33 -0.18038    0.130
##  Carrizo 2023  0.0856 0.0310 33  0.02254    0.149
##  Cuyama  2023  0.0684 0.0491 33 -0.03150    0.168
##  Tecopa  2023  0.1615 0.0763 33  0.00617    0.317
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                            estimate     SE df t.ratio p.value
##  Carrizo year2022 - Cuyama year2022   -0.1116 0.0205 33  -5.440  0.0001
##  Carrizo year2022 - Tecopa year2022    0.1437 0.1092 33   1.316  0.7744
##  Carrizo year2022 - Carrizo year2023   0.0330 0.0153 33   2.165  0.2809
##  Carrizo year2022 - Cuyama year2023    0.0502 0.0205 33   2.446  0.1701
##  Carrizo year2022 - Tecopa year2023   -0.0429 0.1092 33  -0.393  0.9987
##  Cuyama year2022 - Tecopa year2022     0.2553 0.1246 33   2.048  0.3384
##  Cuyama year2022 - Carrizo year2023    0.1446 0.0243 33   5.941  <.0001
##  Cuyama year2022 - Cuyama year2023     0.1618 0.0134 33  12.094  <.0001
##  Cuyama year2022 - Tecopa year2023     0.0687 0.1246 33   0.551  0.9934
##  Tecopa year2022 - Carrizo year2023   -0.1106 0.1056 33  -1.048  0.8980
##  Tecopa year2022 - Cuyama year2023    -0.0935 0.1246 33  -0.750  0.9738
##  Tecopa year2022 - Tecopa year2023    -0.1865 0.0135 33 -13.861  <.0001
##  Carrizo year2023 - Cuyama year2023    0.0172 0.0243 33   0.705  0.9800
##  Carrizo year2023 - Tecopa year2023   -0.0759 0.1056 33  -0.719  0.9782
##  Cuyama year2023 - Tecopa year2023    -0.0931 0.1246 33  -0.747  0.9743
## 
## P value adjustment: tukey method for comparing a family of 6 estimates

Data Viz

### All below in chunk are by site

### Abundance vs Density
abundance <- ggplot(final_data, aes(shrub_density, animals, color = site)) +
  geom_point(size = 0.5) +
  facet_wrap(~year)+
  scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
  labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance 
## `geom_smooth()` using formula = 'y ~ x'

richness <- ggplot(final_data, aes(shrub_density, richness, color = site)) +
  geom_point(size = 0.5) +
  facet_wrap(~year)+
  scale_color_brewer(palette = "Set1") + theme_classic() +  theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
  labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness", color = "Region")
richness
## `geom_smooth()` using formula = 'y ~ x'

Evenness <- ggplot(final_data, aes(shrub_density, Average_Evenness, color = site)) +
  geom_point(size = 0.5) +
  facet_wrap(~year)+
  scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) +  labs(tag = "C")+
  labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
Evenness
## `geom_smooth()` using formula = 'y ~ x'

library(patchwork)
## 
## Attaching package: 'patchwork'
## 
## The following object is masked from 'package:MASS':
## 
##     area
density_plot <- abundance/richness/Evenness
density_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

### Stats for Average Temperature

### Abundance and temperature
model4 <- glm(animals~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data) 
model4
## 
## Call:  glm(formula = animals ~ mean_temp * site * year * mean_humidity, 
##     family = "gaussian", data = final_data)
## 
## Coefficients:
##                             (Intercept)  
##                              -6.423e+08  
##                               mean_temp  
##                               2.677e+07  
##                              siteCuyama  
##                               4.148e+08  
##                              siteTecopa  
##                              -4.661e+08  
##                                    year  
##                               3.175e+05  
##                           mean_humidity  
##                               2.602e+07  
##                    mean_temp:siteCuyama  
##                              -1.626e+07  
##                    mean_temp:siteTecopa  
##                               1.146e+07  
##                          mean_temp:year  
##                              -1.323e+04  
##                         siteCuyama:year  
##                              -2.051e+05  
##                         siteTecopa:year  
##                               2.304e+05  
##                 mean_temp:mean_humidity  
##                              -1.084e+06  
##                siteCuyama:mean_humidity  
##                              -1.764e+07  
##                siteTecopa:mean_humidity  
##                              -7.572e+02  
##                      year:mean_humidity  
##                              -1.286e+04  
##               mean_temp:siteCuyama:year  
##                               8.037e+03  
##               mean_temp:siteTecopa:year  
##                              -5.664e+03  
##      mean_temp:siteCuyama:mean_humidity  
##                               6.983e+05  
##      mean_temp:siteTecopa:mean_humidity  
##                               2.925e+01  
##            mean_temp:year:mean_humidity  
##                               5.360e+02  
##           siteCuyama:year:mean_humidity  
##                               8.722e+03  
##           siteTecopa:year:mean_humidity  
##                                      NA  
## mean_temp:siteCuyama:year:mean_humidity  
##                              -3.452e+02  
## mean_temp:siteTecopa:year:mean_humidity  
##                                      NA  
## 
## Degrees of Freedom: 45 Total (i.e. Null);  24 Residual
## Null Deviance:       765800 
## Residual Deviance: 57540     AIC: 504.6
anova_results4 <- aov(model4, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'test' will be disregarded
anova(model4, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                                   Df Deviance Resid. Df Resid. Dev  Pr(>Chi)
## NULL                                                 45     765773          
## mean_temp                          1    69722        44     696052 6.934e-08
## site                               2    82375        42     613676 3.456e-08
## year                               1   184771        41     428905 < 2.2e-16
## mean_humidity                      1     1138        40     427767 0.4907466
## mean_temp:site                     2   108585        38     319182 1.461e-10
## mean_temp:year                     1     7837        37     311345 0.0706052
## site:year                          2    33153        35     278192 0.0009931
## mean_temp:mean_humidity            1      962        34     277230 0.5264108
## site:mean_humidity                 2     5743        32     271487 0.3018453
## year:mean_humidity                 1     7165        31     264322 0.0838464
## mean_temp:site:year                2     6207        29     258114 0.2739932
## mean_temp:site:mean_humidity       2   160588        27      97526 2.845e-15
## mean_temp:year:mean_humidity       1      367        26      97159 0.6956019
## site:year:mean_humidity            1    36297        25      60862 9.978e-05
## mean_temp:site:year:mean_humidity  1     3326        24      57536 0.2388432
##                                      
## NULL                                 
## mean_temp                         ***
## site                              ***
## year                              ***
## mean_humidity                        
## mean_temp:site                    ***
## mean_temp:year                    .  
## site:year                         ***
## mean_temp:mean_humidity              
## site:mean_humidity                   
## year:mean_humidity                .  
## mean_temp:site:year                  
## mean_temp:site:mean_humidity      ***
## mean_temp:year:mean_humidity         
## site:year:mean_humidity           ***
## mean_temp:site:year:mean_humidity    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results4)
##                              Df Sum Sq Mean Sq F value   Pr(>F)    
## mean_temp                     1  69722   69722  28.639 1.50e-05 ***
## site                          2  82375   41188  16.919 2.26e-05 ***
## year                          1 184771  184771  75.898 4.80e-09 ***
## mean_humidity                 1   1138    1138   0.468 0.500364    
## mean_temp:site                2 108585   54292  22.301 2.76e-06 ***
## mean_temp:year                1   7837    7837   3.219 0.084892 .  
## site:year                     2  33153   16577   6.809 0.004358 ** 
## mean_temp:mean_humidity       1    962     962   0.395 0.535285    
## site:mean_humidity            2   5743    2872   1.180 0.323946    
## year:mean_humidity            1   7165    7165   2.943 0.098614 .  
## mean_temp:site:year           2   6207    3104   1.275 0.297010    
## mean_temp:site:mean_humidity  2 160588   80294  32.982 9.74e-08 ***
## mean_temp:year:mean_humidity  1    367     367   0.151 0.701105    
## site:year:mean_humidity       1  36297   36297  14.910 0.000707 ***
## Residuals                    25  60862    2434                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e5 <- emmeans(model4, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e5
## $emmeans
##  site    year   emmean    SE df lower.CL upper.CL
##  Carrizo 2022   -938.5   619 24    -2217      340
##  Cuyama  2022   1714.0   146 24     1414     2015
##  Tecopa  2022 -89833.5 55883 24  -205170    25503
##  Carrizo 2023     37.5   261 24     -501      576
##  Cuyama  2023   1006.2  1997 24    -3116     5128
##  Tecopa  2023    -81.9  3323 24    -6940     6776
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                            estimate    SE df t.ratio p.value
##  Carrizo year2022 - Cuyama year2022     -2652   636 24  -4.170  0.0041
##  Carrizo year2022 - Tecopa year2022     88895 55335 24   1.606  0.6024
##  Carrizo year2022 - Carrizo year2023     -976   672 24  -1.453  0.6959
##  Carrizo year2022 - Cuyama year2023     -1945  2091 24  -0.930  0.9347
##  Carrizo year2022 - Tecopa year2023      -857  3380 24  -0.253  0.9998
##  Cuyama year2022 - Tecopa year2022      91548 55883 24   1.638  0.5828
##  Cuyama year2022 - Carrizo year2023      1676   299 24   5.615  0.0001
##  Cuyama year2022 - Cuyama year2023        708  2003 24   0.353  0.9992
##  Cuyama year2022 - Tecopa year2023       1796  3326 24   0.540  0.9938
##  Tecopa year2022 - Carrizo year2023    -89871 55876 24  -1.608  0.6012
##  Tecopa year2022 - Cuyama year2023     -90840 55919 24  -1.624  0.5913
##  Tecopa year2022 - Tecopa year2023     -89752 57406 24  -1.563  0.6288
##  Carrizo year2023 - Cuyama year2023      -969  2014 24  -0.481  0.9964
##  Carrizo year2023 - Tecopa year2023       119  3333 24   0.036  1.0000
##  Cuyama year2023 - Tecopa year2023       1088  3877 24   0.281  0.9997
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
### Richness and temperature
model5 <- glm(richness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data) 
model5
## 
## Call:  glm(formula = richness ~ mean_temp * site * year * mean_humidity, 
##     family = "gaussian", data = final_data)
## 
## Coefficients:
##                             (Intercept)  
##                               4.250e+05  
##                               mean_temp  
##                              -2.300e+04  
##                              siteCuyama  
##                               8.940e+06  
##                              siteTecopa  
##                               1.097e+05  
##                                    year  
##                              -2.101e+02  
##                           mean_humidity  
##                              -1.721e+04  
##                    mean_temp:siteCuyama  
##                              -3.999e+05  
##                    mean_temp:siteTecopa  
##                               5.848e+03  
##                          mean_temp:year  
##                               1.137e+01  
##                         siteCuyama:year  
##                              -4.421e+03  
##                         siteTecopa:year  
##                              -5.411e+01  
##                 mean_temp:mean_humidity  
##                               9.181e+02  
##                siteCuyama:mean_humidity  
##                              -2.453e+05  
##                siteTecopa:mean_humidity  
##                               4.210e+00  
##                      year:mean_humidity  
##                               8.507e+00  
##               mean_temp:siteCuyama:year  
##                               1.977e+02  
##               mean_temp:siteTecopa:year  
##                              -2.896e+00  
##      mean_temp:siteCuyama:mean_humidity  
##                               1.097e+04  
##      mean_temp:siteTecopa:mean_humidity  
##                              -3.693e-02  
##            mean_temp:year:mean_humidity  
##                              -4.539e-01  
##           siteCuyama:year:mean_humidity  
##                               1.213e+02  
##           siteTecopa:year:mean_humidity  
##                                      NA  
## mean_temp:siteCuyama:year:mean_humidity  
##                              -5.424e+00  
## mean_temp:siteTecopa:year:mean_humidity  
##                                      NA  
## 
## Degrees of Freedom: 45 Total (i.e. Null);  24 Residual
## Null Deviance:       575.9 
## Residual Deviance: 21.07     AIC: 140.6
anova_results5 <- aov(model5, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'test' will be disregarded
anova(model5, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: richness
## 
## Terms added sequentially (first to last)
## 
## 
##                                   Df Deviance Resid. Df Resid. Dev  Pr(>Chi)
## NULL                                                 45     575.91          
## mean_temp                          1  212.116        44     363.80 < 2.2e-16
## site                               2   13.679        42     350.12 0.0004135
## year                               1  101.722        41     248.40 < 2.2e-16
## mean_humidity                      1   36.989        40     211.41 8.530e-11
## mean_temp:site                     2  105.199        38     106.21 < 2.2e-16
## mean_temp:year                     1   22.336        37      83.87 4.559e-07
## site:year                          2   17.597        35      66.28 4.442e-05
## mean_temp:mean_humidity            1    1.362        34      64.91 0.2129439
## site:mean_humidity                 2    6.395        32      58.52 0.0261978
## year:mean_humidity                 1    0.241        31      58.28 0.6000577
## mean_temp:site:year                2    7.846        29      50.43 0.0114660
## mean_temp:site:mean_humidity       2    3.530        27      46.90 0.1339519
## mean_temp:year:mean_humidity       1   16.254        26      30.65 1.686e-05
## site:year:mean_humidity            1    8.757        25      21.89 0.0015875
## mean_temp:site:year:mean_humidity  1    0.821        24      21.07 0.3334469
##                                      
## NULL                                 
## mean_temp                         ***
## site                              ***
## year                              ***
## mean_humidity                     ***
## mean_temp:site                    ***
## mean_temp:year                    ***
## site:year                         ***
## mean_temp:mean_humidity              
## site:mean_humidity                *  
## year:mean_humidity                   
## mean_temp:site:year               *  
## mean_temp:site:mean_humidity         
## mean_temp:year:mean_humidity      ***
## site:year:mean_humidity           ** 
## mean_temp:site:year:mean_humidity    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results5)
##                              Df Sum Sq Mean Sq F value   Pr(>F)    
## mean_temp                     1 212.12  212.12 242.238 2.27e-14 ***
## site                          2  13.68    6.84   7.811 0.002316 ** 
## year                          1 101.72  101.72 116.168 6.92e-11 ***
## mean_humidity                 1  36.99   36.99  42.241 8.30e-07 ***
## mean_temp:site                2 105.20   52.60  60.069 2.83e-10 ***
## mean_temp:year                1  22.34   22.34  25.508 3.27e-05 ***
## site:year                     2  17.60    8.80  10.048 0.000627 ***
## mean_temp:mean_humidity       1   1.36    1.36   1.555 0.223910    
## site:mean_humidity            2   6.39    3.20   3.652 0.040617 *  
## year:mean_humidity            1   0.24    0.24   0.276 0.604211    
## mean_temp:site:year           2   7.85    3.92   4.480 0.021736 *  
## mean_temp:site:mean_humidity  2   3.53    1.76   2.015 0.154342    
## mean_temp:year:mean_humidity  1  16.25   16.25  18.563 0.000224 ***
## site:year:mean_humidity       1   8.76    8.76  10.000 0.004075 ** 
## Residuals                    25  21.89    0.88                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e6 <- emmeans(model5, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e6
## $emmeans
##  site    year emmean      SE df  lower.CL upper.CL
##  Carrizo 2022  12.10   11.85 24   -12.360    36.56
##  Cuyama  2022   1.88    2.80 24    -3.898     7.67
##  Tecopa  2022 161.29 1069.41 24 -2045.856  2368.43
##  Carrizo 2023  10.67    4.99 24     0.371    20.97
##  Cuyama  2023 142.87   38.22 24    63.983   221.75
##  Tecopa  2023  33.32   63.59 24   -97.914   164.55
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                            estimate      SE df t.ratio p.value
##  Carrizo year2022 - Cuyama year2022     10.22   12.17 24   0.839  0.9570
##  Carrizo year2022 - Tecopa year2022   -149.19 1058.93 24  -0.141  1.0000
##  Carrizo year2022 - Carrizo year2023     1.43   12.86 24   0.111  1.0000
##  Carrizo year2022 - Cuyama year2023   -130.77   40.02 24  -3.268  0.0340
##  Carrizo year2022 - Tecopa year2023    -21.22   64.68 24  -0.328  0.9994
##  Cuyama year2022 - Tecopa year2022    -159.41 1069.41 24  -0.149  1.0000
##  Cuyama year2022 - Carrizo year2023     -8.79    5.71 24  -1.538  0.6444
##  Cuyama year2022 - Cuyama year2023    -140.98   38.32 24  -3.679  0.0133
##  Cuyama year2022 - Tecopa year2023     -31.44   63.65 24  -0.494  0.9959
##  Tecopa year2022 - Carrizo year2023    150.62 1069.27 24   0.141  1.0000
##  Tecopa year2022 - Cuyama year2023      18.42 1070.09 24   0.017  1.0000
##  Tecopa year2022 - Tecopa year2023     127.97 1098.55 24   0.116  1.0000
##  Carrizo year2023 - Cuyama year2023   -132.19   38.54 24  -3.430  0.0236
##  Carrizo year2023 - Tecopa year2023    -22.65   63.78 24  -0.355  0.9992
##  Cuyama year2023 - Tecopa year2023     109.55   74.19 24   1.477  0.6816
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
### Evenness and temperature
model6 <- glm(Average_Evenness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data) 
model6
## 
## Call:  glm(formula = Average_Evenness ~ mean_temp * site * year * mean_humidity, 
##     family = "gaussian", data = final_data)
## 
## Coefficients:
##                             (Intercept)  
##                              -2.924e+04  
##                               mean_temp  
##                               1.075e+03  
##                              siteCuyama  
##                               1.978e+05  
##                              siteTecopa  
##                               1.918e+04  
##                                    year  
##                               1.445e+01  
##                           mean_humidity  
##                               1.089e+03  
##                    mean_temp:siteCuyama  
##                              -8.739e+03  
##                    mean_temp:siteTecopa  
##                              -7.814e+02  
##                          mean_temp:year  
##                              -5.312e-01  
##                         siteCuyama:year  
##                              -9.782e+01  
##                         siteTecopa:year  
##                              -9.486e+00  
##                 mean_temp:mean_humidity  
##                              -3.940e+01  
##                siteCuyama:mean_humidity  
##                              -6.112e+03  
##                siteTecopa:mean_humidity  
##                              -1.112e-02  
##                      year:mean_humidity  
##                              -5.383e-01  
##               mean_temp:siteCuyama:year  
##                               4.321e+00  
##               mean_temp:siteTecopa:year  
##                               3.864e-01  
##      mean_temp:siteCuyama:mean_humidity  
##                               2.686e+02  
##      mean_temp:siteTecopa:mean_humidity  
##                               5.490e-04  
##            mean_temp:year:mean_humidity  
##                               1.948e-02  
##           siteCuyama:year:mean_humidity  
##                               3.022e+00  
##           siteTecopa:year:mean_humidity  
##                                      NA  
## mean_temp:siteCuyama:year:mean_humidity  
##                              -1.328e-01  
## mean_temp:siteTecopa:year:mean_humidity  
##                                      NA  
## 
## Degrees of Freedom: 45 Total (i.e. Null);  24 Residual
## Null Deviance:       0.3064 
## Residual Deviance: 0.001288  AIC: -305.7
anova_results6 <- aov(model6, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'test' will be disregarded
anova(model6, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: Average_Evenness
## 
## Terms added sequentially (first to last)
## 
## 
##                                   Df Deviance Resid. Df Resid. Dev  Pr(>Chi)
## NULL                                                 45   0.306353          
## mean_temp                          1 0.011046        44   0.295307 < 2.2e-16
## site                               2 0.073313        42   0.221994 < 2.2e-16
## year                               1 0.068416        41   0.153577 < 2.2e-16
## mean_humidity                      1 0.004958        40   0.148620 < 2.2e-16
## mean_temp:site                     2 0.112108        38   0.036512 < 2.2e-16
## mean_temp:year                     1 0.002065        37   0.034447 5.578e-10
## site:year                          2 0.012030        35   0.022417 < 2.2e-16
## mean_temp:mean_humidity            1 0.000145        34   0.022272 0.1001135
## site:mean_humidity                 2 0.000774        32   0.021498 0.0007366
## year:mean_humidity                 1 0.001450        31   0.020048 2.032e-07
## mean_temp:site:year                2 0.003008        29   0.017040 6.819e-13
## mean_temp:site:mean_humidity       2 0.005407        27   0.011633 < 2.2e-16
## mean_temp:year:mean_humidity       1 0.007064        26   0.004569 < 2.2e-16
## site:year:mean_humidity            1 0.002788        25   0.001781 5.712e-13
## mean_temp:site:year:mean_humidity  1 0.000492        24   0.001288 0.0024604
##                                      
## NULL                                 
## mean_temp                         ***
## site                              ***
## year                              ***
## mean_humidity                     ***
## mean_temp:site                    ***
## mean_temp:year                    ***
## site:year                         ***
## mean_temp:mean_humidity              
## site:mean_humidity                ***
## year:mean_humidity                ***
## mean_temp:site:year               ***
## mean_temp:site:mean_humidity      ***
## mean_temp:year:mean_humidity      ***
## site:year:mean_humidity           ***
## mean_temp:site:year:mean_humidity ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results6)
##                              Df  Sum Sq Mean Sq F value   Pr(>F)    
## mean_temp                     1 0.01105 0.01105 155.089 3.23e-12 ***
## site                          2 0.07331 0.03666 514.655  < 2e-16 ***
## year                          1 0.06842 0.06842 960.555  < 2e-16 ***
## mean_humidity                 1 0.00496 0.00496  69.605 1.08e-08 ***
## mean_temp:site                2 0.11211 0.05605 786.988  < 2e-16 ***
## mean_temp:year                1 0.00206 0.00206  28.990 1.38e-05 ***
## site:year                     2 0.01203 0.00602  84.452 7.58e-12 ***
## mean_temp:mean_humidity       1 0.00015 0.00015   2.038 0.165804    
## site:mean_humidity            2 0.00077 0.00039   5.437 0.010954 *  
## year:mean_humidity            1 0.00145 0.00145  20.352 0.000132 ***
## mean_temp:site:year           2 0.00301 0.00150  21.114 4.26e-06 ***
## mean_temp:site:mean_humidity  2 0.00541 0.00270  37.958 2.66e-08 ***
## mean_temp:year:mean_humidity  1 0.00706 0.00706  99.178 3.48e-10 ***
## site:year:mean_humidity       1 0.00279 0.00279  39.150 1.52e-06 ***
## Residuals                    25 0.00178 0.00007                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e7 <- emmeans(model6, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e7
## $emmeans
##  site    year  emmean     SE df lower.CL upper.CL
##  Carrizo 2022  0.2333 0.0927 24   0.0420   0.4246
##  Cuyama  2022 -0.0603 0.0218 24  -0.1054  -0.0153
##  Tecopa  2022 -0.0817 8.3624 24 -17.3409  17.1775
##  Carrizo 2023  0.0399 0.0390 24  -0.0407   0.1204
##  Cuyama  2023  2.0244 0.2989 24   1.4075   2.6413
##  Tecopa  2023 -0.0993 0.4972 24  -1.1255   0.9269
## 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                            estimate     SE df t.ratio p.value
##  Carrizo year2022 - Cuyama year2022    0.2937 0.0952 24   3.084  0.0508
##  Carrizo year2022 - Tecopa year2022    0.3150 8.2805 24   0.038  1.0000
##  Carrizo year2022 - Carrizo year2023   0.1935 0.1005 24   1.924  0.4125
##  Carrizo year2022 - Cuyama year2023   -1.7911 0.3129 24  -5.724  0.0001
##  Carrizo year2022 - Tecopa year2023    0.3326 0.5058 24   0.658  0.9849
##  Cuyama year2022 - Tecopa year2022     0.0213 8.3625 24   0.003  1.0000
##  Cuyama year2022 - Carrizo year2023   -0.1002 0.0447 24  -2.241  0.2565
##  Cuyama year2022 - Cuyama year2023    -2.0848 0.2997 24  -6.957  <.0001
##  Cuyama year2022 - Tecopa year2023     0.0390 0.4977 24   0.078  1.0000
##  Tecopa year2022 - Carrizo year2023   -0.1215 8.3614 24  -0.015  1.0000
##  Tecopa year2022 - Cuyama year2023    -2.1061 8.3678 24  -0.252  0.9998
##  Tecopa year2022 - Tecopa year2023     0.0176 8.5903 24   0.002  1.0000
##  Carrizo year2023 - Cuyama year2023   -1.9846 0.3014 24  -6.584  <.0001
##  Carrizo year2023 - Tecopa year2023    0.1392 0.4987 24   0.279  0.9997
##  Cuyama year2023 - Tecopa year2023     2.1237 0.5801 24   3.661  0.0139
## 
## P value adjustment: tukey method for comparing a family of 6 estimates
### Ambient Temperature Plots

### Plots of temp might look super ugly.

abundance_temp <- ggplot(final_data, aes(mean_temp, animals)) +
  geom_point(size = 0.5) +
  facet_wrap(~year, scales = "free")+ 
  scale_color_brewer(palette = "Set1") +
  labs(x = "Average Temperature (C)", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")
abundance_temp
## `geom_smooth()` using formula = 'y ~ x'

richness_temp <- ggplot(final_data, aes(mean_temp, richness)) +
  geom_point(size = 0.5) +
  facet_wrap(~year, scales = "free")+
  scale_color_brewer(palette = "Set1") + theme(axis.title.x = element_blank()) + labs(tag = "B")+ theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) +
  labs(x = "Average Temperature (C)", y = "Richness") + theme(axis.title.x = element_blank())
richness_temp
## `geom_smooth()` using formula = 'y ~ x'

evenness_temp <- ggplot(final_data, aes(mean_temp, Average_Evenness)) +
  geom_point(size = 0.5) +
  facet_wrap(~year, scales = "free")+
  scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) +  labs(tag = "C")+
  labs(x = expression("Mean Temperature (°C)"), y = "Mean Evenness")
evenness_temp
## `geom_smooth()` using formula = 'y ~ x'

temp_plot <- abundance_temp/richness_temp/evenness_temp
temp_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

### Other posisble figure options

abundance_2 <- ggplot(final_data, aes(shrub_density, animals)) + geom_point(size = 0.5) +
  facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
  labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance_2
## `geom_smooth()` using formula = 'y ~ x'

richness_2 <- ggplot(final_data, aes(shrub_density, richness)) + geom_point(size = 0.5) +
  facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
  labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness")
richness_2
## `geom_smooth()` using formula = 'y ~ x'

evenness_2 <- ggplot(final_data, aes(shrub_density, Average_Evenness)) + geom_point(size = 0.5) +
  facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
  geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
  labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")

plot1.1 <- abundance_2/richness_2/evenness_2
plot1.1
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

### Percent proportion Figure
#write.csv(density_obvs_final, file = "Animal Observations.csv")
scientific_names <- read.csv("Animal Observations.csv")
plot3 <- ggplot(scientific_names, aes(scientific_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot3 <-plot3 + scale_fill_manual(values = c("#009900", "#0066cc"))
# Assuming you have a dataset named scientific_names with columns: scientific_name, percent_presence, microsite

# Create a new variable to specify the ordering based on presence in density or open areas
scientific_names$microsite <- ifelse(scientific_names$microsite == "Density", "Shrub", scientific_names$microsite)

scientific_names <- scientific_names %>%
  mutate(ordering_var = ifelse(microsite == "Shrub", 1, 2)) %>%
  arrange(ordering_var, desc(percent_presence))



# Create the histogram-style figure with reordering
plot3.2 <- ggplot(scientific_names, aes(fct_inorder(scientific_name), percent_presence, fill = microsite)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  theme_classic() +
  xlab("Species") +
  ylab("Percent Proportion") +
  labs(fill = "Microsite") +
  scale_fill_manual(values = c("#0066cc", "#009900")) + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) 

# Reorder the x-axis labels
plot3.2 <- plot3.2 + scale_x_discrete(limits = rev(levels(scientific_names$scientific_name))) 

# Plot the figure
print(plot3.2)

Final Figures for Publication

### Figure 1: Abundance, Richness, Evenness vs Shrub Density
plot1.1
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

### Figure 2: Abundance, Richness, Evenness vs Average Temperature
temp_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'

### Figure 3: PCOA of Communities
pcoa_final

### Figure 4: Percent Proportion of Vertebrate species
plot3.2

#write.csv(final_data, file = "Final Data.csv")

Data Together

### Test figure with formula (Shrub Density)
final_data <- final_data %>%
  mutate(year = as.character(year))

### Density v abundance
ggplot(final_data, aes(shrub_density, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "A")

### Density v Richness
ggplot(final_data, aes(shrub_density, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "B")

### Density v Evenness
ggplot(final_data, aes(shrub_density, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "C")

### Aridity v Abundance
ggplot(final_data, aes(aridity, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") +  labs(x = "Aridity", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "A")

### Aridity v Richness
ggplot(final_data, aes(aridity, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") +  labs(x = "Aridity", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "B")

### Aridity v Evenness
ggplot(final_data, aes(aridity, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") +  labs(x = "Aridity", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "C")

### Stats
#install.packages("lme4", type = "source")
library(lmerTest)
## Loading required package: lme4
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack
## 
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
## 
##     lmer
## The following object is masked from 'package:stats':
## 
##     step
library(performance)
#simple
m1 <- lmer(animals ~ shrub_density + (1|year) + aridity, data = final_data)
check_collinearity(m1)
## # Check for Multicollinearity
## 
## Low Correlation
## 
##           Term  VIF       VIF 95% CI Increased SE Tolerance Tolerance 95% CI
##  shrub_density 1.01 [1.00, 2.65e+12]         1.00      0.99     [0.00, 1.00]
##        aridity 1.01 [1.00, 2.65e+12]         1.00      0.99     [0.00, 1.00]
anova(m1)
## Type III Analysis of Variance Table with Satterthwaite's method
##               Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)   
## shrub_density  14370   14370     1 42.018  1.2164 0.27635   
## aridity       114226  114226     1 42.010  9.6692 0.00336 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeansLT(m1)
## Least Squares Means table:
## 
##      Estimate Std. Error df t value lower upper Pr(>|t|)
## 
##   Confidence level: 95%
##   Degrees of freedom method: Satterthwaite
ranova(m1) # I think this is to test for random effects across year?
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## animals ~ shrub_density + aridity + (1 | year)
##            npar  logLik    AIC    LRT Df Pr(>Chisq)  
## <none>        5 -271.70 553.40                       
## (1 | year)    4 -274.97 557.94 6.5368  1    0.01057 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m2 <- lmer(richness ~ shrub_density + (1|year) + aridity, data = final_data)
## boundary (singular) fit: see help('isSingular')
check_collinearity(m2)
## # Check for Multicollinearity
## 
## Low Correlation
## 
##           Term  VIF       VIF 95% CI Increased SE Tolerance Tolerance 95% CI
##  shrub_density 1.01 [1.00, 7.65e+12]         1.00      0.99     [0.00, 1.00]
##        aridity 1.01 [1.00, 7.65e+12]         1.00      0.99     [0.00, 1.00]
anova(m2)
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## shrub_density  17.096  17.096     1    43  1.8397 0.1820664    
## aridity       149.184 149.184     1    43 16.0534 0.0002401 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ranova(m2)
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## richness ~ shrub_density + aridity + (1 | year)
##            npar  logLik    AIC         LRT Df Pr(>Chisq)
## <none>        5 -116.83 243.66                          
## (1 | year)    4 -116.83 241.66 -8.5265e-14  1          1
m3 <- lmer(Average_Evenness ~ shrub_density + (1|year) + aridity, data = final_data)
## boundary (singular) fit: see help('isSingular')
check_collinearity(m3)
## # Check for Multicollinearity
## 
## Low Correlation
## 
##           Term  VIF       VIF 95% CI Increased SE Tolerance Tolerance 95% CI
##  shrub_density 1.01 [1.00, 7.65e+12]         1.00      0.99     [0.00, 1.00]
##        aridity 1.01 [1.00, 7.65e+12]         1.00      0.99     [0.00, 1.00]
anova(m3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)  
## shrub_density 0.0215532 0.0215532     1    43  3.2712 0.0775 .
## aridity       0.0026079 0.0026079     1    43  0.3958 0.5326  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ranova(m3)
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## Average_Evenness ~ shrub_density + aridity + (1 | year)
##            npar logLik     AIC         LRT Df Pr(>Chisq)
## <none>        5 39.082 -68.164                          
## (1 | year)    4 39.082 -70.164 -1.4211e-14  1          1

Test Trophic Level?

trophic <- read.csv("Animal Observations.csv")


ggplot(trophic, aes(microsite, captures, color = Trophic)) +
geom_boxplot() +
scale_color_brewer(palette = "Set1") +  labs(x = "Aridity", y = "Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "C")

m9<- glm(total ~ microsite * Trophic, family = "poisson", data = trophic)
anova(m9, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: total
## 
## Terms added sequentially (first to last)
## 
## 
##                   Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                 55      27821              
## microsite          1     29.6        54      27791 5.449e-08 ***
## Trophic            2   7884.0        52      19907 < 2.2e-16 ***
## microsite:Trophic  2      2.0        50      19905     0.376    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e9 <- emmeans(m9, pairwise~microsite|Trophic)
e9
## $emmeans
## Trophic = Carnivore:
##  microsite emmean     SE  df asymp.LCL asymp.UCL
##  Density     3.06 0.0767 Inf      2.91      3.21
##  Open        3.18 0.0769 Inf      3.03      3.33
## 
## Trophic = Herbivore:
##  microsite emmean     SE  df asymp.LCL asymp.UCL
##  Density     5.62 0.0173 Inf      5.59      5.66
##  Open        5.71 0.0174 Inf      5.68      5.74
## 
## Trophic = Omnivore:
##  microsite emmean     SE  df asymp.LCL asymp.UCL
##  Density     3.06 0.0685 Inf      2.92      3.19
##  Open        3.00 0.0788 Inf      2.85      3.16
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## Trophic = Carnivore:
##  contrast       estimate     SE  df z.ratio p.value
##  Density - Open  -0.1276 0.1086 Inf  -1.175  0.2400
## 
## Trophic = Herbivore:
##  contrast       estimate     SE  df z.ratio p.value
##  Density - Open  -0.0849 0.0245 Inf  -3.461  0.0005
## 
## Trophic = Omnivore:
##  contrast       estimate     SE  df z.ratio p.value
##  Density - Open   0.0567 0.1044 Inf   0.543  0.5869
## 
## Results are given on the log (not the response) scale.

Seperate everything by year (Nonlinear better for 2022)

final_data_2022 <- final_data %>%
  filter(year == "2022")

shapiro.test(final_data_2022$animals)
## 
##  Shapiro-Wilk normality test
## 
## data:  final_data_2022$animals
## W = 0.76017, p-value = 7.037e-05
ggqqplot(final_data_2022$animals)

n1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n1
## 
## Call:  glm(formula = animals ~ shrub_density * aridity, family = "gaussian", 
##     data = final_data_2022)
## 
## Coefficients:
##           (Intercept)          shrub_density                aridity  
##              -27.7891                 0.6522                54.0811  
## shrub_density:aridity  
##                1.6648  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  20 Residual
## Null Deviance:       619900 
## Residual Deviance: 376700    AIC: 310
anova(n1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     23     619927              
## shrub_density          1    26978        22     592949 0.2314117    
## aridity                1   211738        21     381211 0.0008004 ***
## shrub_density:aridity  1     4465        20     376746 0.6263640    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n1), "\n")
## Linear Model AIC: 309.9796
quadratic_model_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_1
## 
## Call:  glm(formula = animals ~ shrub_density + I(shrub_density^2) + 
##     aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", 
##     data = final_data_2022)
## 
## Coefficients:
##           (Intercept)          shrub_density     I(shrub_density^2)  
##               139.405                100.932                 -9.768  
##               aridity           I(aridity^2)  shrub_density:aridity  
##              -412.549                117.917                  6.412  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  18 Residual
## Null Deviance:       619900 
## Residual Deviance: 186500    AIC: 297.1
anova(quadratic_model_1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
## NULL                                     23     619927             
## shrub_density          1    26978        22     592949 0.106605    
## I(shrub_density^2)     1     2942        21     590007 0.594141    
## aridity                1   261283        20     328724 5.12e-07 ***
## I(aridity^2)           1    89956        19     238768 0.003213 ** 
## shrub_density:aridity  1    52272        18     186496 0.024696 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_1), "\n")
## Quadratic Model AIC: 297.1038
if (AIC(quadratic_model_1) < AIC(n1)) {
  cat("Quadratic model is better.\n")
} else {
  cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
abund_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, animals)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))

### Richness
n2 <- glm(richness ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n2
## 
## Call:  glm(formula = richness ~ shrub_density * aridity, family = "gaussian", 
##     data = final_data_2022)
## 
## Coefficients:
##           (Intercept)          shrub_density                aridity  
##              -0.34946                0.08662                2.58054  
## shrub_density:aridity  
##              -0.01174  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  20 Residual
## Null Deviance:       475 
## Residual Deviance: 132.4     AIC: 119.1
anova(n2, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: richness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     23     474.96              
## shrub_density          1     6.43        22     468.53    0.3243    
## aridity                1   335.94        21     132.58 1.043e-12 ***
## shrub_density:aridity  1     0.22        20     132.36    0.8546    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n2), "\n")
## Linear Model AIC: 119.0888
quadratic_model_2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_2
## 
## Call:  glm(formula = richness ~ shrub_density + I(shrub_density^2) + 
##     aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", 
##     data = final_data_2022)
## 
## Coefficients:
##           (Intercept)          shrub_density     I(shrub_density^2)  
##                1.9865                -0.7054                 0.0776  
##               aridity           I(aridity^2)  shrub_density:aridity  
##               -3.8810                 1.6566                -0.0559  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  18 Residual
## Null Deviance:       475 
## Residual Deviance: 87.2  AIC: 113.1
anova(quadratic_model_2, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: richness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     23     474.96              
## shrub_density          1    6.431        22     468.53   0.24925    
## I(shrub_density^2)     1  106.430        21     362.10 2.769e-06 ***
## aridity                1  247.045        20     115.05 9.248e-13 ***
## I(aridity^2)           1   23.883        19      91.17   0.02639 *  
## shrub_density:aridity  1    3.972        18      87.20   0.36517    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")
## Quadratic Model AIC: 113.0717
if (AIC(quadratic_model_2) < AIC(n2)) {
  cat("Quadratic model is better.\n")
} else {
  cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
rich_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, richness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))

### Evenness
n3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
n3
## 
## Call:  glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
## 
## Coefficients:
##           (Intercept)          shrub_density                aridity  
##              0.019618              -0.001800               0.030276  
## shrub_density:aridity  
##              0.001651  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  20 Residual
## Null Deviance:       0.1455 
## Residual Deviance: 0.05188   AIC: -69.18
anova(n3, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: Average_Evenness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     23   0.145519              
## shrub_density          1 0.007333        22   0.138186    0.0927 .  
## aridity                1 0.081917        21   0.056269 1.913e-08 ***
## shrub_density:aridity  1 0.004391        20   0.051878    0.1932    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n3), "\n")
## Linear Model AIC: -69.17676
quadratic_model_3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_3
## 
## Call:  glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) + 
##     aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", 
##     data = final_data_2022)
## 
## Coefficients:
##           (Intercept)          shrub_density     I(shrub_density^2)  
##             0.0446964             -0.0295177              0.0027085  
##               aridity           I(aridity^2)  shrub_density:aridity  
##            -0.0385879              0.0178654              0.0002091  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  18 Residual
## Null Deviance:       0.1455 
## Residual Deviance: 0.03225   AIC: -76.59
anova(quadratic_model_3, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: Average_Evenness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     23   0.145519              
## shrub_density          1 0.007333        22   0.138186   0.04307 *  
## I(shrub_density^2)     1 0.056383        21   0.081804 2.027e-08 ***
## aridity                1 0.047100        20   0.034703 2.941e-07 ***
## I(aridity^2)           1 0.002397        19   0.032306   0.24743    
## shrub_density:aridity  1 0.000056        18   0.032251   0.86020    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")
## Quadratic Model AIC: 113.0717
if (AIC(quadratic_model_2) < AIC(n2)) {
  cat("Quadratic model is better.\n")
} else {
  cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
even_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
   labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))

plot <- abund_dens_2022/rich_dens_2022/even_dens_2022
plot

### Temperature (Hold off on this and ask chris)

t1 <- glm(animals ~ shrub_density * mean_temp, family = "gaussian", data = final_data_2022)
t1
## 
## Call:  glm(formula = animals ~ shrub_density * mean_temp, family = "gaussian", 
##     data = final_data_2022)
## 
## Coefficients:
##             (Intercept)            shrub_density                mean_temp  
##                641.3669                  22.3664                 -20.8447  
## shrub_density:mean_temp  
##                 -0.6221  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  20 Residual
## Null Deviance:       619900 
## Residual Deviance: 389100    AIC: 310.8
anova(t1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev Pr(>Chi)   
## NULL                                       23     619927            
## shrub_density            1    26978        22     592949 0.238985   
## mean_temp                1   199846        21     393103 0.001351 **
## shrub_density:mean_temp  1     3970        20     389134 0.651496   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(t1), "\n")
## Linear Model AIC: 310.756
quadratic_temp_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian", data = final_data_2022)
quadratic_temp_1
## 
## Call:  glm(formula = animals ~ shrub_density + I(shrub_density^2) + 
##     mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian", 
##     data = final_data_2022)
## 
## Coefficients:
##             (Intercept)            shrub_density       I(shrub_density^2)  
##                4363.574                  155.190                   -6.801  
##               mean_temp           I(mean_temp^2)  shrub_density:mean_temp  
##                -305.916                    5.317                   -2.618  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  18 Residual
## Null Deviance:       619900 
## Residual Deviance: 301200    AIC: 308.6
anova(quadratic_temp_1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                       23     619927              
## shrub_density            1    26978        22     592949 0.2042037    
## I(shrub_density^2)       1     2942        21     590007 0.6750263    
## mean_temp                1   240180        20     349827 0.0001516 ***
## I(mean_temp^2)           1     2772        19     347055 0.6840117    
## shrub_density:mean_temp  1    45820        18     301235 0.0979909 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_temp_1), "\n")
## Quadratic Model AIC: 308.6113

Shrub Density 2023 (Linear better for 2023)

final_data_2023 <- final_data %>%
  filter(year == "2023")

x1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2023)
x1
## 
## Call:  glm(formula = animals ~ shrub_density * aridity, family = "gaussian", 
##     data = final_data_2023)
## 
## Coefficients:
##           (Intercept)          shrub_density                aridity  
##               8.32431                1.85904                2.30955  
## shrub_density:aridity  
##              -0.05488  
## 
## Degrees of Freedom: 21 Total (i.e. Null);  18 Residual
## Null Deviance:       12650 
## Residual Deviance: 10190     AIC: 207.5
anova(x1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                                     21      12649           
## shrub_density          1  2257.72        20      10391  0.04579 *
## aridity                1   199.04        19      10192  0.55317  
## shrub_density:aridity  1     4.64        18      10187  0.92782  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x1), "\n")
## Linear Model AIC: 207.4663
quadratic_model_x1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2023)
quadratic_model_x1
## 
## Call:  glm(formula = animals ~ shrub_density + I(shrub_density^2) + 
##     aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", 
##     data = final_data_2023)
## 
## Coefficients:
##           (Intercept)          shrub_density     I(shrub_density^2)  
##              -1.65760                3.43942               -0.15541  
##               aridity           I(aridity^2)  shrub_density:aridity  
##              29.97243               -7.06338                0.03991  
## 
## Degrees of Freedom: 21 Total (i.e. Null);  16 Residual
## Null Deviance:       12650 
## Residual Deviance: 9619  AIC: 210.2
anova(quadratic_model_x1, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: animals
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev Pr(>Chi)  
## NULL                                     21    12648.8           
## shrub_density          1  2257.72        20    10391.1  0.05264 .
## I(shrub_density^2)     1    55.46        19    10335.6  0.76135  
## aridity                1   317.46        18    10018.1  0.46743  
## I(aridity^2)           1   396.75        17     9621.4  0.41659  
## shrub_density:aridity  1     1.93        16     9619.5  0.95482  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x1), "\n")
## Quadratic Model AIC: 210.2043
if (AIC(quadratic_model_x1) < AIC(x1)) {
  cat("Quadratic model is better.\n")
} else {
  cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
abund_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, animals)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
abund_dens_2023
## `geom_smooth()` using formula = 'y ~ x'

x2 <- glm(richness ~ shrub_density * aridity, data = final_data_2023)
x2
## 
## Call:  glm(formula = richness ~ shrub_density * aridity, data = final_data_2023)
## 
## Coefficients:
##           (Intercept)          shrub_density                aridity  
##              4.775057               0.220144              -0.167510  
## shrub_density:aridity  
##             -0.009058  
## 
## Degrees of Freedom: 21 Total (i.e. Null);  18 Residual
## Null Deviance:       95.32 
## Residual Deviance: 67.28     AIC: 97.03
anova(x2, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: richness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev Pr(>Chi)   
## NULL                                     21     95.318            
## shrub_density          1  25.4984        20     69.820 0.009006 **
## aridity                1   2.4110        19     67.409 0.421898   
## shrub_density:aridity  1   0.1265        18     67.282 0.854019   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x2), "\n")
## Linear Model AIC: 97.02606
quadratic_model_x2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x2
## 
## Call:  glm(formula = richness ~ shrub_density + I(shrub_density^2) + 
##     aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
## 
## Coefficients:
##           (Intercept)          shrub_density     I(shrub_density^2)  
##               3.32139               -0.05358                0.02657  
##               aridity           I(aridity^2)  shrub_density:aridity  
##               3.87515               -1.02486               -0.02127  
## 
## Degrees of Freedom: 21 Total (i.e. Null);  16 Residual
## Null Deviance:       95.32 
## Residual Deviance: 59.25     AIC: 98.23
anova(quadratic_model_x2, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: richness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev Pr(>Chi)   
## NULL                                     21     95.318            
## shrub_density          1  25.4984        20     69.820 0.008689 **
## I(shrub_density^2)     1   0.4031        19     69.417 0.741448   
## aridity                1   2.0180        18     67.399 0.460389   
## I(aridity^2)           1   7.6014        17     59.797 0.151936   
## shrub_density:aridity  1   0.5479        16     59.249 0.700486   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x2), "\n")
## Quadratic Model AIC: 98.22897
if (AIC(quadratic_model_x2) < AIC(x2)) {
  cat("Quadratic model is better.\n")
} else {
  cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
rich_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, richness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
  theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
rich_dens_2023
## `geom_smooth()` using formula = 'y ~ x'

x3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
x3
## 
## Call:  glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
## 
## Coefficients:
##           (Intercept)          shrub_density                aridity  
##             0.1816823              0.0084948             -0.0469045  
## shrub_density:aridity  
##            -0.0009284  
## 
## Degrees of Freedom: 21 Total (i.e. Null);  18 Residual
## Null Deviance:       0.1608 
## Residual Deviance: 0.009233  AIC: -98.64
anova(x3, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: Average_Evenness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     21   0.160797              
## shrub_density          1 0.013715        20   0.147082  2.33e-07 ***
## aridity                1 0.136519        19   0.010562 < 2.2e-16 ***
## shrub_density:aridity  1 0.001329        18   0.009233    0.1074    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x3), "\n")
## Linear Model AIC: -98.63904
quadratic_model_x3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x3
## 
## Call:  glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) + 
##     aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
## 
## Coefficients:
##           (Intercept)          shrub_density     I(shrub_density^2)  
##             0.1963905              0.0105327             -0.0001974  
##               aridity           I(aridity^2)  shrub_density:aridity  
##            -0.0877876              0.0103750             -0.0008426  
## 
## Degrees of Freedom: 21 Total (i.e. Null);  16 Residual
## Null Deviance:       0.1608 
## Residual Deviance: 0.008405  AIC: -96.71
anova(quadratic_model_x3, test = "Chisq")
## Analysis of Deviance Table
## 
## Model: gaussian, link: identity
## 
## Response: Average_Evenness
## 
## Terms added sequentially (first to last)
## 
## 
##                       Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    
## NULL                                     21   0.160797              
## shrub_density          1 0.013715        20   0.147082 3.226e-07 ***
## I(shrub_density^2)     1 0.020159        19   0.126922 5.833e-10 ***
## aridity                1 0.116561        18   0.010361 < 2.2e-16 ***
## I(aridity^2)           1 0.001096        17   0.009265    0.1485    
## shrub_density:aridity  1 0.000860        16   0.008405    0.2006    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x3), "\n")
## Quadratic Model AIC: -96.7066
if (AIC(quadratic_model_x1) < AIC(x1)) {
  cat("Quadratic model is better.\n")
} else {
  cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
even_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") +  labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
   labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
even_dens_2023
## `geom_smooth()` using formula = 'y ~ x'

plot2 <- abund_dens_2023/rich_dens_2023/even_dens_2023
plot2
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'